Learning Race from Face: A Survey
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paper_title: FRVT 2006 and ICE 2006 Large-Scale Experimental Results

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This paper describes the large-scale experimental results from the Face Recognition Vendor Test (FRVT) 2006 and the Iris Challenge Evaluation (ICE) 2006. The FRVT 2006 looked at recognition from high-resolution still frontal face images and 3D face images, and measured performance for still frontal face images taken under controlled and uncontrolled illumination. The ICE 2006 evaluation reported verification performance for both left and right irises. The images in the ICE 2006 intentionally represent a broader range of quality than the ICE 2006 sensor would normally acquire. This includes images that did not pass the quality control software embedded in the sensor. The FRVT 2006 results from controlled still and 3D images document at least an order-of-magnitude improvement in recognition performance over the FRVT 2002. The FRVT 2006 and the ICE 2006 compared recognition performance from high-resolution still frontal face images, 3D face images, and the single-iris images. On the FRVT 2006 and the ICE 2006 data sets, recognition performance was comparable for high-resolution frontal face, 3D face, and the iris images. In an experiment comparing human and algorithms on matching face identity across changes in illumination on frontal face images, the best performing algorithms were more accurate than humans on unfamiliar faces.
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paper_title: A neural mechanism of first impressions

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Evaluating social others requires processing complex information. Nevertheless, we can rapidly form an opinion of an individual during an initial encounter. Moreover, people can vary in these opinions, even though the same information is provided. We investigated the brain mechanisms that give rise to the impressions that are formed on meeting a new person. Neuroimaging revealed that responses in the amygdala and the posterior cingulate cortex (PCC) were stronger while encoding social information that was consistent, relative to inconsistent, with subsequent evaluations. In addition, these responses scaled parametrically with the strength of evaluations. These findings provide evidence for encoding differences on the basis of subsequent evaluations, suggesting that the amygdala and PCC are important for forming first impressions.
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paper_title: Structural encoding and identification in face processing: ERP evidence for separate mechanisms

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The present study had two aims. The first aim was to explore the possible top-down effect of facerecognition and/or face-identification processes on the formation of structural representation of faces, as indexed by the N170 ERP component. The second aim was to examine possible ERP manifestations of face identification processes as an initial step for assessing their time course and functional neuroanatomy. Identical N170 potentials were elicited by famous and unfamiliar faces in Experiment 1, when both were irrelevant to the task, suggesting that face familiarity does not affect structural encoding processes. Small but significant differences were observed, however, during later-occurring epochs of the ERPs. In Experiment 2 the participants were instructed to count occasionally occurring portraits of famous politicians while rejecting faces of famous people who were not politicians and faces of unfamiliar people. Although an attempt to identify each face was required, no differences were found in the N170 elicited by faces of unfamiliar people and faces of familiar non-politicians. Famous faces, however, elicited a negative potential that was significantly larger than that elicited by unfamiliar faces between about 250 and 500msec from stimulus onset. This negative component was tentatively identified as an N400 analogue elicited by faces. Both the absence of an effect of familiarity on the N170 and the familiarity face-N400 effect were replicated in Experiment 3, in which the participants made speeded button-press responses in each trial, distinguishing among faces of politicians and faces of famous and unfamiliar non-politicians. In addition, ERP components later than the N400 were found to be associated with the speed of the response but not with face familiarity. We concluded that (1) although reflected by the N170, the structural encoding mechanism is not influenced by the face recognition and identification processes, and (2) the negative component modulated by face familiarity is associated with the semantic activity involved in the identification of familiar faces.
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paper_title: Classifying faces by race: The structure of face categories.

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This article explored the finding that cross-race (CR) faces are more quickly classified by race than same race (SR) faces. T. Valentine and M. Endo (1992) modeled this effect by assuming that face categories can be explained on the basis of node activations in a multidimensional exemplar space. Therefore, variations in exemplar density between and within face categories explain both facilitated classification of CR faces and the relationship between typicality and classification RT within face categories. The present findings from classification and visual search tasks suggest that speeded classification of CR faces is instead caused by a quickly coded race feature that marks CR but not SR faces. Also, systematic manipulations of facial typicality cause no variation in classifiability aside from slowed classification of very distinctive faces. These results suggest that the exemplar model cannot explain important aspects of face classification. Although face perception is usually studied from the standpoint of our amazing ability to differentiate a large number of faces, representations of face categories are also important. The process of categorizing individual faces has a number of implications both for general models of classification and for understanding face identification. The focus here is on the apparently paradoxical finding that participants are faster to classify faces they have difficulty recognizing. In the present case, this means that White participants classify Black or Asian faces faster than White faces (Levin, 1989; Valentine & Endo, 1992). In attempting to understand facilitated classification of cross-race faces (hereinafter referred to as the CR [cross-race] classification advantage), the present research considers explanations for the CR classification advantage as they relate to the basic structure of face categories, both in terms of discrimination between categories and in terms of their internal structure. Three explanations for the CR classification advantage are tested here. The first, stemming from Valentine's (1991) multidimensional space framework, places the advantage in the context of an exemplar model of face classification and recognition. This model uses simple assumptions based on
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paper_title: Believing Is Seeing: The Effects of Racial Labels and Implicit Beliefs on Face Perception

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Two studies tested whether racial category labels and lay beliefs about human traits have a combined effect on people’s perception of, and memory for, racially ambiguous faces. Participants saw a morphed target face accompanied by a racial label (Black or White). Later, they were asked to identify the face from a set of two new morphed faces, one more Black and the other more White than the target. As predicted, entity theorists, who believe traits are immutable, perceived and remembered the target face as consistent with the racial label, whereas incremental theorists, who believe traits are malleable, perceived and remembered the face as inconsistent with the racial label. In Study 2, participants also drew the target face more consistently (entity theorists) or less consistently (incremental theorists) with the racial label. Results of both studies confirm that social variables can affect how physical features are seen and remembered.
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paper_title: Overview of the face recognition grand challenge

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Over the last couple of years, face recognition researchers have been developing new techniques. These developments are being fueled by advances in computer vision techniques, computer design, sensor design, and interest in fielding face recognition systems. Such advances hold the promise of reducing the error rate in face recognition systems by an order of magnitude over Face Recognition Vendor Test (FRVT) 2002 results. The face recognition grand challenge (FRGC) is designed to achieve this performance goal by presenting to researchers a six-experiment challenge problem along with data corpus of 50,000 images. The data consists of 3D scans and high resolution still imagery taken under controlled and uncontrolled conditions. This paper describes the challenge problem, data corpus, and presents baseline performance and preliminary results on natural statistics of facial imagery.
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paper_title: An evaluation of multimodal 2D+3D face biometrics

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We report on the largest experimental study to date in multimodal 2D+3D face recognition, involving 198 persons in the gallery and either 198 or 670 time-lapse probe images. PCA-based methods are used separately for each modality and match scores in the separate face spaces are combined for multimodal recognition. Major conclusions are: 1) 2D and 3D have similar recognition performance when considered individually, 2) combining 2D and 3D results using a simple weighting scheme outperforms either 2D or 3D alone, 3) combining results from two or more 2D images using a similar weighting scheme also outperforms a single 2D image, and 4) combined 2D+3D outperforms the multi-image 2D result. This is the first (so far, only) work to present such an experimental control to substantiate multimodal performance improvement.
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paper_title: Head-and-Face Anthropometric Survey of U.S. Respirator Users

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Sizing data generated by the military for use in fitting respirators have been the normative basis for commercial respirator sizing. Anthropometric data developed for males and females of military age in the 1950s and 1960s are still in use today and form the only comprehensive body of information available on this subject. The twofold objective of this study was to: (1) develop an anthropometric database detailing the face size distributions of respirator users using both traditional measurement methods and three-dimensional scanning systems; and (2) use the database to establish fit test panels to be incorporated into the National Institute for Occupational Safety and Health's respirator certification and international standards. A stratified sampling plan was used with three age strata, two gender strata, and four race/ethnic group strata. The plan called for an equal sample size of 166 in each cell. Subjects were obtained at 41 sites from 8 states. In addition to height and weight, 18 facial dimension...
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paper_title: The face-sensitive N170 encodes social category information

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Prominent models of face perception posit that the encoding of social categories begins after initial structural encoding has completed. In contrast, we hypothesized that social category encoding may occur simultaneously with structural encoding. While event-related potentials were recorded, participants categorized the sex of sex-typical and sex-atypical faces. Results indicated that the face-sensitive right N170, a component involved in structural encoding, was larger for sex-typical relative to sex-atypical faces. Moreover, its amplitude predicted the efficiency of sex-category judgments. The right P1 component also peaked earlier for sex-typical faces. These findings show that social category encoding and the extraction of lower-level face information operate in parallel, suggesting that they may be accomplished by a single dynamic process rather than two separate mechanisms.
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paper_title: A survey of affect recognition methods: audio, visual and spontaneous expressions

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Automated analysis of human affective behavior has attracted increasing attention from researchers in psychology, computer science, linguistics, neuroscience, and related disciplines. Promising approaches have been reported, including automatic methods for facial and vocal affect recognition. However, the existing methods typically handle only deliberately displayed and exaggerated expressions of prototypical emotions-despite the fact that deliberate behavior differs in visual and audio expressions from spontaneously occurring behavior. Recently efforts to develop algorithms that can process naturally occurring human affective behavior have emerged. This paper surveys these efforts. We first discuss human emotion perception from a psychological perspective. Next, we examine the available approaches to solving the problem of machine understanding of human affective behavior occurring in real-world settings. We finally outline some scientific and engineering challenges for advancing human affect sensing technology.
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paper_title: The role of age and ethnic group in face recognition memory: ERP evidence from a combined own-age and own-race bias study

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Abstract Young adult participants are known to more accurately remember faces from both their own age- and ethnic groups. The present study examined combined effects of such own-age and own-race biases by asking young Caucasian participants to learn and remember elderly and young Caucasian as well as elderly and young Asian faces. Neural correlates were assessed by recording event-related potentials (ERPs). Behavioral results indicated both an own-race bias for young but not elderly faces, and an own-age bias for Caucasian but not Asian faces. Importantly, no additional decrease in recognition memory for other-race/other-age faces was detected. An early parietal ERP old/new effect (300–500 ms) was most pronounced for young Caucasian “in-group” faces, while the old/new effect in a later time window (500–800 ms) was generally larger for own- as compared to other-race faces. In conclusion, these findings suggest at least partly different neural processes to accompany the own-race and own-age biases.
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paper_title: Person Categorization and Automatic Racial Stereotyping Effects on Weapon Identification

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Prior stereotyping research provides conflicting evidence regarding the importance of person categorization along a particular dimension for the automatic activation of a stereotype corresponding to that dimension. Experiment 1 replicated a racial stereotyping effect on object identification and examined whether it could be attenuated by encouraging categorization by age. Experiment 2 employed socially complex person stimuli and manipulated whether participants categorized spontaneously or by race. In Experiment 3, the distinctiveness of the racial dimension was manipulated by having Black females appear in the context of either Black males or White females. The results indicated that conditions fostering categorization by race consistently produced automatic racial stereotyping and that conditions fostering nonracial categorization can eliminate automatic racial stereotyping. Implications for the relation between automatic stereotype activation and dimension of categorization are discussed.
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paper_title: Pan-Cultural Elements in Facial Displays of Emotion

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Observers in both literate and preliterate cultures chose the predicted emotion for photographs of the face, although agreement was higher in the literate samples. These findings suggest that the pan-cultural element in facial displays of emotion is the association between facial muscular movements and discrete primary emotions, although cultures may still differ in what evokes an emotion, in rules for controlling the display of emotion, and in behavioral consequences.
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paper_title: Age Synthesis and Estimation via Faces: A Survey

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Human age, as an important personal trait, can be directly inferred by distinct patterns emerging from the facial appearance. Derived from rapid advances in computer graphics and machine vision, computer-based age synthesis and estimation via faces have become particularly prevalent topics recently because of their explosively emerging real-world applications, such as forensic art, electronic customer relationship management, security control and surveillance monitoring, biometrics, entertainment, and cosmetology. Age synthesis is defined to rerender a face image aesthetically with natural aging and rejuvenating effects on the individual face. Age estimation is defined to label a face image automatically with the exact age (year) or the age group (year range) of the individual face. Because of their particularity and complexity, both problems are attractive yet challenging to computer-based application system designers. Large efforts from both academia and industry have been devoted in the last a few decades. In this paper, we survey the complete state-of-the-art techniques in the face image-based age synthesis and estimation topics. Existing models, popular algorithms, system performances, technical difficulties, popular face aging databases, evaluation protocols, and promising future directions are also provided with systematic discussions.
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paper_title: Perception and Motivation in Face Recognition: A Critical Review of Theories of the Cross-Race Effect

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Although humans possess well-developed face processing expertise, face processing is nevertheless subject to a variety of biases. Perhaps the best known of these biases is the Cross-Race Effect—the tendency to have more accurate recognition for same-race than cross-race faces. The current work reviews the evidence for and provides a critical review of theories of the Cross-Race Effect, including perceptual expertise and social cognitive accounts of the bias. The authors conclude that recent hybrid models of the Cross-Race Effect, which combine elements of both perceptual expertise and social cognitive frameworks, provide an opportunity for theoretical synthesis and advancement not afforded by independent expertise or social cognitive models. Finally, the authors suggest future research directions intended to further develop a comprehensive and integrative understanding of biases in face recognition.
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paper_title: Vision-based Human Gender Recognition: A Survey

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Gender is an important demographic attribute of people. This paper provides a survey of human gender recognition in computer vision. A review of approaches exploiting information from face and whole body (either from a still image or gait sequence) is presented. We highlight the challenges faced and survey the representative methods of these approaches. Based on the results, good performance have been achieved for datasets captured under controlled environments, but there is still much work that can be done to improve the robustness of gender recognition under real-life environments.
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paper_title: Automatic recognition and analysis of human faces and facial expressions: a survey

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Abstract Humans detect and identify faces in a scene with little or no effort. However, building an automated system that accomplishes this task is very difficult. There are several related subproblems: detection of a pattern as a face, identification of the face, analysis of facial expressions, and classification based on physical features of the face. A system that performs these operations will find many applications, e.g. criminal identification, authentication in secure systems, etc. Most of the work to date has been in identification. This paper surveys the past work in solving these problems. The capability of the human visual system with respect to these problems is also discussed. It is meant to serve as a guide for an automated system. Some new approaches to these problems are also briefly discussed.
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paper_title: The Categorization-Individuation Model: An Integrative Account of the Other-Race Recognition Deficit.

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The other-race effect (ORE), or the finding that same-race faces are better recognized than other-race faces, is one of the best replicated phenomena in face recognition. The current article reviews existing evidence and theory and proposes a new theoretical framework for the ORE, which argues that the effect results from a confluence of social categorization, motivated individuation, and perceptual experience. This categorization-individuation model offers not only a parsimonious account of both classic and recent evidence for category-based biases in face recognition but also links the ORE to broader evidence and theory in social cognition and face perception. Finally, the categorization-individuation model makes a series of novel predictions for how the ORE can be exacerbated, attenuated, or even eliminated via perceptual and motivational processes, both by improving other-race recognition and by reducing same-race recognition. The authors propose that this new model for the ORE also leads to applied interventions that differ sharply from other theories of the ORE, while simultaneously providing an integrative theoretical framework for future research on the ORE.
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paper_title: THIRTY YEARS OF INVESTIGATING THE OWN-RACE BIAS IN MEMORY FOR FACES A Meta-Analytic Review

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The current article reviews the own-race bias (ORB) phenomenon in memory for human faces, the finding that own-race faces are better remembered when compared with memory for faces of another, less familiar race. Data were analyzed from 39 research articles, involving 91 independent samples and nearly 5,000 participants. Measures of hit and false alarm rates, and aggregate measures of discriminatio n accuracy and response criterion were examined, including an analysis of 8 study moderators. Several theoretical relationships were also assessed (i.e., the influence of racial attitudes and interracial contact). Overall, results indicated a "mirror effect" pattern in which own-race faces yielded a higher proportion of hits and a lower proportion of false alarms compared with other-race faces. Consistent with this effect, a significant ORB was also found in aggregate measures of discriminatio n accuracy and response criterion. The influence of perceptual learning and differentiation processes in the ORB are discussed, in addition to the practical implications of this phenomenon. She based her identification on Smith's eyes, which she said were greenish-blue and upon his hands which she said were "light and slender" like the holdup man's. Mrs. McCormick testified that Smith's eyes were "different from most colored people ... bright and piercing." Smith's defense attorneys then attempted to parry the state's first thrust in the trial. Mrs. McCormick was handed a picture of a man she couldn't identify. It was a picture of David Charles, with shorter hair, taken while he was in Vietnam. Assistant defense attorney Kitchen asked Mrs. McCormick if she had ever made the statement that all Black people look alike. "Yes, I made that statement," Mrs. McCormick said, "and they do to a certain extent, but there's a difference here" (Lickson, 1974, p. 66). In 1971, five Black men, who became known as the "Quincy Five," were wrongfully indicted for the murder of Khomas Revels during a robbery in Tallahassee, Florida. Although no forensic evidence obtained from the crime scene was ever linked to the men, five White eyewitnesses positively identified them as among the perpetrators. In each of three trials the state argued, "What better evidence can there be than, 'I saw him,' from unprejudiced witnesses? This has been used since time immemorial. This is proof beyond a reasonable doubt. Five eyewitnesses!" (Lickson, 1974, p. 87). Despite the lack of physical evidence against these men, two of the defendants, Dave Roby Keaton and Johnny Frederick, were found guilty on the basis of eyewitness testimony and coerced confessions obtained by investigators . During the third trial involving David
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paper_title: 2D and 3D face recognition: A survey

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Government agencies are investing a considerable amount of resources into improving security systems as result of recent terrorist events that dangerously exposed flaws and weaknesses in today's safety mechanisms. Badge or password-based authentication procedures are too easy to hack. Biometrics represents a valid alternative but they suffer of drawbacks as well. Iris scanning, for example, is very reliable but too intrusive; fingerprints are socially accepted, but not applicable to non-consentient people. On the other hand, face recognition represents a good compromise between what's socially acceptable and what's reliable, even when operating under controlled conditions. In last decade, many algorithms based on linear/nonlinear methods, neural networks, wavelets, etc. have been proposed. Nevertheless, Face Recognition Vendor Test 2002 shown that most of these approaches encountered problems in outdoor conditions. This lowered their reliability compared to state of the art biometrics. This paper provides an ''ex cursus'' of recent face recognition research trends in 2D imagery and 3D model based algorithms. To simplify comparisons across different approaches, tables containing different collection of parameters (such as input size, recognition rate, number of addressed problems) are provided. This paper concludes by proposing possible future directions.
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paper_title: The neuroscience of race

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The authors review recent research examining how social categories of race and ethnicity are processed, evaluated and incorporated in decision-making. They also speculate about the potential of future work to inform how we recognize and respond to variations in race and its influence on unintended race-based attitudes and decisions.
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paper_title: Automatic facial expression analysis: a survey

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Over the last decade, automatic facial expression analysis has become an active research area that finds potential applications in areas such as more engaging human-computer interfaces, talking heads, image retrieval and human emotion analysis. Facial expressions reflect not only emotions, but other mental activities, social interaction and physiological signals. In this survey we introduce the most prominent automatic facial expression analysis methods and systems presented in the literature. Facial motion and deformation extraction approaches as well as classification methods are discussed with respect to issues such as face normalization, facial expression dynamics and facial expression intensity, but also with regard to their robustness towards environmental changes.
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paper_title: A Developmental Intergroup Theory of Social Stereotypes and Prejudice

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Publisher Summary This chapter proposes a new theory of the formation of social stereotypes and prejudice among children: developmental intergroup theory (DIT). It defines the terms “stereotyping” and “prejudice” to specify the phenomena that the theory is designed to address and outlines the importance of considering the ontogenetic emergence of stereotyping and prejudice. The core characteristics and goals of the theory are described and the key components of intergroup and cognitive-developmental theories that provide foundations for the theoretical model are highlighted. The chapter deals with the mechanisms that hypothesize account for the formation of stereotypes and prejudice among children as a group, emphasizing how the theory differs from other major theories of stereotyping and prejudice. It reviews the environmental, developmental, and individual differences that contribute to variations in stereotyping and prejudice across children. The chapter also discusses the implications of developmental intergroup theory both for research and social action to reduce stereotyping and prejudice.
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paper_title: Shape analysis of 3D head scan data for U.S. respirator users

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In 2003, the National Institute for Occupational Safety and Health (NIOSH) conducted a head-and-face anthropometric survey of diverse, civilian respirator users. Of the 3,997 subjects measured using traditional anthropometric techniques, surface scans and 26 three-dimensional (3D) landmark locations were collected for 947 subjects. The objective of this study was to report the size and shape variation of the survey participants using the 3D data. Generalized Procrustes Analysis (GPA) was conducted to standardize configurations of landmarks associated with individuals into a common coordinate system. The superimposed coordinates for each individual were used as commensurate variables that describe individual shape and were analyzed using Principal Component Analysis (PCA) to identify population variation. The first four principal components (PC) account for 49% of the total sample variation. The first PC indicates that overall size is an important component of facial variability. The second PC accounts for long and narrow or short and wide faces. Longer narrow orbits versus shorter wider orbits can be described by PC3, and PC4 represents variation in the degree of ortho/prognathism. Geometric Morphometrics provides a detailed and interpretable assessment of morphological variation that may be useful in assessing respirators and devising new test and certification standards.
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paper_title: Recognizing moving faces: A psychological and neural synthesis

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Information for identifying a human face can be found both in the invariant structure of features and in idiosyncratic movements and gestures. When both kinds of information are available, psychological evidence indicates that: (1) dynamic information contributes more to recognition under non-optimal viewing conditions, e.g. poor illumination, low image resolution, recognition from a distance; (2) dynamic information contributes more as a viewer's experience with the face increases; and (3) a structure-from-motion analysis can make a perceptually based contribution to face recognition. A recently proposed distributed neural system for face perception, with minor modifications, can accommodate the psychological findings with moving faces.
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paper_title: The N170: Understanding the Time Course of Face Perception in the Human Brain

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This chapter reviews the contribution of electromagnetic measures, mostly event-related potentials (ERPs), to our understanding of the time course of face processing in the normal adult brain, with a focus on the 100–200 ms time window after stimulus onset, that is, during during the occipitotemporal component termed the N170. It fi rst describes the N170 component, how it can be defi ned, and its relationship to the vertex positive potential (VPP) response to faces that was reported prior to the N170 in the literature. It then addresses the question of the origin of the largest N170 to faces in terms of electroencephalographic (EEG) signal, neural sources, and functional processes that lead to this effect. It also discusses the controversial issue of whether the N170 refl ects underlying processes that can be at least partly recruited for processing nonface objects following extensive visual experience with these objects. The chapter summarizes the evidence showing that the N170 refl ects both the initial basic-level categorization of the stimulus as a face through the activation of neural face representations and the coding of individual face representations. It then briefl y discusses why the N170 may be a critical time window for other types of face categorizations before summarizing the chapter and addressing the question of how the N170 can be taken as a tool to clarify the dynamics and the nature of early face processes in future research.
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paper_title: Sociology: Your Compass for a New World

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Intended for the introduction to sociology course found in departments of sociology and social sciences at two- and four-year schools. The course usually fulfills a general education requirement, so in addition to the many sociology majors taking the course are many nonmajors. Regardless of their major, most students taking this course are freshmen and sophomores.
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paper_title: From theory to implementation: Building a multidimensional space for face recognition

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Abstract The purpose of the present study was to empirically construct a multidimensional model of face space based upon Valentine’s [Valentine, T. (1991). A unified account of the effects of distinctiveness, inversion, and race in face recognition. Quarterly Journal of Experimental Psychology, 43A, 161–204; Valentine, T. (2001). Face-space models of face recognition. In M. J. Wenger, & J. T. Townsend, (Eds.). Computational, geometric, and process perspectives on facial cognition: Contexts and challenges. Scientific psychology series (pp. 83–113). Mahwah, NJ: Erlbaum] metaphoric model. Two-hundred and ten participants ranked 200 faces on a 21-dimensional space composed of internal facial features. On the basis of these dimensions an index of distance from the center of the dimensional space was calculated. A factor analysis revealed six factors which highlighted the importance of both featural and holistic processes in face recognition. Testing the model in relation to facial distinctiveness and face recognition strengthened its validity by emphasizing the relevance of the constructed multidimensional space for face recognition. The data are discussed within the framework of theoretical models of face recognition.
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paper_title: Oxford Handbook of Face Perception

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Book synopsis: The first ever comprehensive review of the field of face perception, providing a much needed reference work for students and researchers in the brain sciences ::: Includes cutting edge chapters, written and edited by an international team of top researchers ::: Illustrated in full colour throughout ::: The human face is unique among social stimuli in conveying such a variety of different characteristics. A person's identity, sex, race, age, emotional state, focus of attention, facial speech patterns, and attractiveness are all detected and interpreted with relative ease from the face. Humans also display a surprising degree of consistency in the extent to which personality traits, such as trustworthiness and likeability, are attributed to faces. In the past thirty years, face perception has become an area of major interest within psychology, with a rapidly expanding research base. Yet until now, there has been no comprehensive reference work bringing together this ever growing body of research. ::: ::: The Oxford Handbook of Face Perception is the most comprehensive and commanding review of the field ever published. It looks at the functional and neural mechanisms underlying the perception, representation, and interpretation of facial characteristics, such as identity, expression, eye gaze, attractiveness, personality, and race. It examines the development of these processes, their neural correlates in both human and non-human primates, congenital and acquired disorders resulting from their breakdown, and the theoretical and computational frameworks for their underlying mechanisms. With chapters by an international team of leading authorities from the brain sciences, the book is a landmark publication on face perception.
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paper_title: Handbook of Face Recognition

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This highly anticipated new edition provides a comprehensive account of face recognition research and technology, spanning the full range of topics needed for designing operational face recognition systems. After a thorough introductory chapter, each of the following chapters focus on a specific topic, reviewing background information, up-to-date techniques, and recent results, as well as offering challenges and future directions. Features: fully updated, revised and expanded, covering the entire spectrum of concepts, methods, and algorithms for automated face detection and recognition systems; provides comprehensive coverage of face detection, tracking, alignment, feature extraction, and recognition technologies, and issues in evaluation, systems, security, and applications; contains numerous step-by-step algorithms; describes a broad range of applications; presents contributions from an international selection of experts; integrates numerous supporting graphs, tables, charts, and performance data.
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paper_title: Can race be erased? Coalitional computation and social categorization

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Previous studies have established that people encode the race of each individual they encounter, and do so via computational processes that appear to be both automatic and mandatory. If true, this conclusion would be important, because categorizing others by their race is a precondition for treating them differently according to race. Here we report experiments, using unobtrusive measures, showing that categorizing individuals by race is not inevitable, and supporting an alternative hypothesis: that encoding by race is instead a reversible byproduct of cognitive machinery that evolved to detect coalitional alliances. The results show that subjects encode coalitional affiliations as a normal part of person representation. More importantly, when cues of coalitional affiliation no longer track or correspond to race, subjects markedly reduce the extent to which they categorize others by race, and indeed may cease doing so entirely. Despite a lifetime's experience of race as a predictor of social alliance, less than 4 min of exposure to an alternate social world was enough to deflate the tendency to categorize by race. These results suggest that racism may be a volatile and eradicable construct that persists only so long as it is actively maintained through being linked to parallel systems of social alliance.
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paper_title: THE CHINESE FACE: A 3D ANTHROPOMETRIC ANALYSIS

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Traditional facial anthropometry has a long history yet few studies have been conducted using the latest 3D scanning technology. The majority of these facial studies have focused on Caucasian populations. The recent SizeChina survey has collected high resolution 3D data from over 2000 adult Chinese subjects. The 3D facial data of 772 females from the SizeChina database were analyzed in this study. This data was pre-processed to eliminate scan noise, fill holes caused by shadow effect and aligned to a common plane. Thirty one facial landmarks were used for statistical analysis and to create a 3D anthropometric face model. Simple statistics results demonstrated that Chinese females in general have wider and shorter faces compared to other cultures. Correlation analysis showed the breadth dimensions are highly correlated and it seems that heavier people have larger bigonial breadth. Results from the principal component analysis on anthropometric face model reveals that major variations of face shapes are related to face breadth, face depth, face length, chin and jaw position. The results of this study have significant implications for the design of facial products for Chinese women.
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paper_title: Perspectives on Racial Phenotypicality Bias

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This article reviews research examining racial phenotypicality bias—within-category stereotypes, prejudice, and discrimination based on race-related phenotypic characteristics of the face. A literature review of research examining skin tone bias, drawing largely from work examining perceptions of Blacks in the United States, reveals that individuals with features typical of members of their racial category are perceived and treated more negatively by social perceivers. Furthermore, this treatment has broad implications for social status and health. Despite this evidence, the tendency to attend to and use within-race variation in phenotypic appearance has been over-looked in social psychological models of impression formation. However, several theoretical frameworks have recently been proposed to explain the role of phenotype-based expectancies in social representation and judgment. Drawing on the strengths of each perspective, a rudimentary model of racial phenotypicality bias is proposed. This analysis s...
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paper_title: Perceptions of race

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Until recently, experiments on person perception had led to two unwelcome conclusions: (1) people encode the race of each individual they encounter, and (2) race encoding is caused by computational mechanisms whose operation is automatic and mandatory. Evolutionary analyses rule out the hypothesis that the brain mechanisms that cause race encoding evolved for that purpose. Consequently, race encoding must be a byproduct of mechanisms that evolved for some alternative function. But which one? Race is not encoded as a byproduct of domain-general perceptual processes. Two families of byproduct hypotheses remain: one invokes inferential machinery designed for tracking coalitional alliances, the other machinery designed for reasoning about natural kinds. Recent experiments show that manipulating coalitional variables can dramatically decrease the extent to which race is noticed and remembered.
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paper_title: A survey of approaches and challenges in 3D and multi-modal 3D + 2D face recognition

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This survey focuses on recognition performed by matching models of the three-dimensional shape of the face, either alone or in combination with matching corresponding two-dimensional intensity images. Research trends to date are summarized, and challenges confronting the development of more accurate three-dimensional face recognition are identified. These challenges include the need for better sensors, improved recognition algorithms, and more rigorous experimental methodology.
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paper_title: A comparison between Chinese and Caucasian head shapes.

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Univariate anthropometric data have long documented a difference in head shape proportion between Chinese and Caucasian populations. This difference has made it impossible to create eyewear, helmets and facemasks that fit both groups well. However, it has been unknown to what extend and precisely how the two populations differ from each other in form. In this study, we applied geometric morphometrics to dense surface data to quantify and characterize the shape differences using a large data set from two recent 3D anthropometric surveys, one in North America and Europe, and one in China. The comparison showed the significant variations between head shapes of the two groups and results demonstrated that Chinese heads were rounder than Caucasian counterparts, with a flatter back and forehead. The quantitative measurements and analyses of these shape differences may be applied in many fields, including anthropometrics, product design, cranial surgery and cranial therapy.
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paper_title: Categorical perception of race.

paper_content:

Traditionally, research demonstrating categorical perception (CP) has assumed that CP occurs only in cases where natural continua are divided categorically by long-term learning or innate perceptual programming. More recent research suggests that this may not be true, and that even novel continua between novel stimuli such as unfamiliar faces can show CP effects as well. Given this, we ask whether CP is dependent solely on the representation of individual stimuli, or whether stimulus categories themselves can also cause CP. Here, we test the hypothesis that continua between individual faces that cross the categorical boundary between races show an enhanced CP effect. We find that continua running from a black face to a white face do, indeed, show stronger CP effects than continua between two black faces or two white faces. This suggests that CP effects are enhanced when continua run between two distinctly represented individual stimuli, and are further enhanced when those individuals are, in turn, members...
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paper_title: Race as a visual feature: Using visual search and perceptual discrimination tasks to understand face categories and the cross-race recognition deficit.

paper_content:

One of the most familiar empirical phenomena associated with face recognition is the cross-race (CR) recognition deficit whereby people have difficulty recognizing members of a race different from their own. Most researchers assume that the CR deficit is caused by failure to generalize perceptual encoding expertise from same-race (SR) faces to CR faces. However, this explanation ignores critical differences in the social cognitions and feature coding priorities associated with SR and CR faces. On the basis of data from visual search and perceptual discrimination tasks, it appears that the deficit occurs because people emphasize visual information specifying race at the expense of individuating information when recognizing CR faces. In particular, it is possible to observe a paradoxical improvement in both detection and perceptual discrimination accuracy for CR faces that is limited to those who recognize them poorly. These findings support a new explanation for the CR recognition deficit based on feature coding differences between CR and SR faces, and appear incompatible with similarity-based models of face categories.
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paper_title: Social Cognition: Thinking Categorically about Others

paper_content:

In attempting to make sense of other people, perceivers regularly construct and use categorical representations to simplify and streamline the person perception process. Noting the importance of categorical thinking in everyday life, our emphasis in this chapter is on the cognitive dynamics of categorical social perception. In reviewing current research on this topic, three specific issues are addressed: (a) When are social categories activated by perceivers, (b) what are the typical consequences of category activation, and (c) can perceivers control the influence and expression of categorical thinking? Throughout the chapter, we consider how integrative models of cognitive functioning may inform our understanding of categorical social perception.
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paper_title: No role for lightness in the perception of black and white? Simultaneous contrast affects perceived skin tone, but not perceived race

paper_content:

Faces of individuals with African and European heritage (henceforth referred to as Black and White respectively) feature two major differences: those of skin tone and morphological characteristics. Although considerations of perceived race are important to various psychological subdisciplines, to date the relative influence of morphological versus photometric characteristics has not been investigated. We attempted to influence the perceived racial typicality of a central target face by manipulating perceived skin tone using the well-known lightness contrast illusion. As expected, ratings of skin tone were influenced by surround faces, yet ratings of perceived racial typicality were not, suggesting a dissociation between the two judgments. Surprisingly, skin tone contributes little to perceived race, leaving facial morphology as the dominant cue. These results may shed light on failures to find effects of racial typicality in studies of prejudice where judgments were based on photographs with altered skin ...
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paper_title: Haplotype Variation and Linkage Disequilibrium in 313 Human Genes

paper_content:

Variation within genes has important implications for all biological traits. We identified 3899 single nucleotide polymorphisms (SNPs) that were present within 313 genes from 82 unrelated individuals of diverse ancestry, and we organized the SNPs into 4304 different haplotypes. Each gene had several variable SNPs and haplotypes that were present in all populations, as well as a number that were population-specific. Pairs of SNPs exhibited variability in the degree of linkage disequilibrium that was a function of their location within a gene, distance from each other, population distribution, and population frequency. Haplotypes generally had more information content (heterozygosity) than did individual SNPs. Our analysis of the pattern of variation strongly supports the recent expansion of the human population.
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paper_title: Human races: Classifying people vs. understanding diversity

paper_content:

The idea that all humans naturally belong to one of a few biological types or races that evolved in isolation was unchallenged for centuries, but large-scale modern studies failed to associate racial labels with recognizable genetic clus- ters. Recently, the conclusions of those studies have been questioned by authors who argue that racial classification has objective scientific bases and is indispensable in epidemiology and genetics. However, no classificatio n is useful if the classification units are vague or controversial , and no consensus was ever reached on the number and definition of the human races. The available studies show that there is geographic structure in human genome diversity, and that it is possi- ble to infer with reasonable accuracy the continent of origin from an individual's multilocus genotype. However, clear-cut genetic boundaries between human groups, which would be necessary to recognise these groups as relatively isolated mating units which zoologists would call races, have not been identified so far. On the contrary, allele frequencies and synthetic descriptors of genetic variation appear distributed in gradients over much of the planet, which points to gene flow, rather than to isolation, as the main evolutionary force shaping human genome diversity. A better understanding of patterns of human diversity and of the underlying evolutionary processes is important for its own sake, but is also indis- pensable for the development of diagnostic and therapeutic tools designed for the individual genotype, rather than for ill- defined race-specific genotypes.
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paper_title: Perceiving the sex and race of faces: the role of shape and colour

paper_content:

Theories of object recognition have emphasized the information conveyed by shape information, whereas theories of face recognition have emphasized properties of superficial features. In the experiments reported here we used novel technology to investigate the relative contributions of shape and superficial colour information to simple categorization decisions about the sex and ‘race’ of faces. The results show that both shape and colour provide useful information for these decisions; shape information was particularly useful for race decisions while colour dominated sex decisions. When both sources of information were combined, the dominant source depended on viewpoint, with angled views emphasizing the contribution of shape and the full-face view colour. The results are discussed within the context of theories of face recognition and their implications for telecommunication applications are considered.
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paper_title: A robust approach to facial ethnicity classification on large scale face databases

paper_content:

In this paper, we propose a novel approach using Kernel Class-dependent Feature Analysis (KCFA) combined with facial color based features to tackle the problem of ethnicity classification on large scale face databases. In our approach, a new design of multiple filtered responses of the Kernel Class-dependent Feature Analysis is used for ethnicity classes. In order to improve the robustness of our system, the facial color based features are also employed to incorporate with the filtered responses to extract the stable ethnicity features. Compared to previous approaches, our proposed method achieves the best accuracy of ethnicity classification on large industrial based face databases and the standard MBGC face database.
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paper_title: Oxford Handbook of Face Perception

paper_content:

Book synopsis: The first ever comprehensive review of the field of face perception, providing a much needed reference work for students and researchers in the brain sciences ::: Includes cutting edge chapters, written and edited by an international team of top researchers ::: Illustrated in full colour throughout ::: The human face is unique among social stimuli in conveying such a variety of different characteristics. A person's identity, sex, race, age, emotional state, focus of attention, facial speech patterns, and attractiveness are all detected and interpreted with relative ease from the face. Humans also display a surprising degree of consistency in the extent to which personality traits, such as trustworthiness and likeability, are attributed to faces. In the past thirty years, face perception has become an area of major interest within psychology, with a rapidly expanding research base. Yet until now, there has been no comprehensive reference work bringing together this ever growing body of research. ::: ::: The Oxford Handbook of Face Perception is the most comprehensive and commanding review of the field ever published. It looks at the functional and neural mechanisms underlying the perception, representation, and interpretation of facial characteristics, such as identity, expression, eye gaze, attractiveness, personality, and race. It examines the development of these processes, their neural correlates in both human and non-human primates, congenital and acquired disorders resulting from their breakdown, and the theoretical and computational frameworks for their underlying mechanisms. With chapters by an international team of leading authorities from the brain sciences, the book is a landmark publication on face perception.
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paper_title: Skin and Bones: The Contribution of Skin Tone and Facial Structure to Racial Prototypicality Ratings

paper_content:

Previous research reveals that a more ‘African’ appearance has significant social consequences, yielding more negative first impressions and harsher criminal sentencing of Black or White individuals. This study is the first to systematically assess the relative contribution of skin tone and facial metrics to White, Black, and Korean perceivers’ ratings of the racial prototypicality of faces from the same three groups. Our results revealed that the relative contribution of metrics and skin tone depended on both perceiver race and face race. White perceivers’ racial prototypicality ratings were less responsive to variations in skin tone than were Black or Korean perceivers’ ratings. White perceivers ratings’ also were more responsive to facial metrics than to skin tone, while the reverse was true for Black perceivers. Additionally, across all perceiver groups, skin tone had a more consistent impact than metrics on racial prototypicality ratings of White faces, with the reverse for Korean faces. For Black faces, the relative impact varied with perceiver race: skin tone had a more consistent impact than metrics for Black and Korean perceivers, with the reverse for White perceivers. These results have significant implications for predicting who will experience racial prototypicality biases and from whom.
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paper_title: Minimizing Skin Color Differences Does Not Eliminate the Own-Race Recognition Advantage in Infants.

paper_content:

An abundance of experience with own-race faces and limited to no experience with other-race faces has been associated with better recognition memory for own-race faces in infants, children, and adults. This study investigated the developmental origins of this other-race effect (ORE) by examining the role of a salient perceptual property of faces—that of skin color. Six- and 9-month-olds’ recognition memory for own- and other-race faces was examined using infant-controlled habituation and visual-paired comparison at test. Infants were shown own- or other-race faces in color or with skin color cues minimized in grayscale images. Results for the color stimuli replicated previous findings that infants show an ORE in face recognition memory. Results for the grayscale stimuli showed that even when a salient perceptual cue to race, such as skin color information, is minimized, 6- to 9-month-olds, nonetheless, show an ORE in their face recognition memory. Infants’ use of shape-based and configural cues for face recognition is discussed.
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paper_title: Fuzzy Moments Method for Face Recognition in an Ethnic Database

paper_content:

So far, numerous methods have been proposed for face recognition based on single image person. In this paper, we introduce a new recognition algorithm based on feature vectors of three types of moments and fuzzy combination of them. We used an ethnic database in our experiments that are distinctly different from other famous databases such as AR and FERET. In ethnic database women have Hijab that is important feature in Muslim community such as Arabian countries. Also most of men have beard or mustache in these nations. Experimental results show that these features increase the recognition rate. Really, these characteristics eliminate the false recognition between male and female in the process. Subsets of 100 images from ethnic, AR and FERET are used in our experiments. Proposed method achieves 96% accuracy for ethnic database that is higher than two other databases.
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paper_title: Categorization of humans in biomedical research: genes, race and disease

paper_content:

A debate has arisen regarding the validity of racial/ethnic categories for biomedical and genetic research. An epidemiologic perspective on the issue of human categorization in biomedical and genetic research strongly supports the continued use of self-identified race and ethnicity.
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paper_title: Race Classification Based on Facial Features

paper_content:

Human is unique and is an ancestral gift. Biometric recognition system can use race in identifying the human globally with a particular identity. This paper proposes an algorithm for classification of people into three major races namely Caucasoid, Negroid and Mongoloid, which can easily be integrated into any face recognition system. Firstly, the proposed classification method extracts the distinct primary facial feature, skin color model, and the other secondary features such as forehead and lip of the given face to effectively classify the races. Extensive simulations on images obtained from Yale, FERET and other sources exhibit superior performance of the proposed method.
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paper_title: Towards race-related face identification: research on skin color transfer

paper_content:

We developed a novel skin-color transfer algorithm in order to explore the potential information from human racial groups to improve the face identification. We attempt to find a general technique for transferring human skin colors of different races. The idea is to match the intensity values of two images and transfer the entire mood of the source image to the target image. Further enhancement can be achieved in two ways. One is facial multi-region subdivision to limit the color transfer between corresponding regions. The other is to use "region growing" method based on hue values for color transfer in sub-areas, such as lip, where H channel is dominant in determining pixel colors. The technique appears to be quite simple and results turn out to be encouraging. Our new algorithm is analyzed and compared to other existing algorithm, and evaluated through the psychological study on race related identification. The experiment shows that skin color is amongst one of the factors for race identification, but not the dominant one. This is the first preliminary step as an initial screening process towards the race-related face identification.
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paper_title: Computational perspectives on the other-race effect

paper_content:

Psychological studies have long shown that human memory is superior for faces of our own-race than for faces of other-races. In this paper, we review computational studies of own- versus other-race face processing. Computational models examine the visual challenges of representing the uniqueness of individual faces that vary both within and across demographic categories. These models isolate the visual components of the other-race effect and provide an objective control for socioaffective responses to other-race faces. This control allows researchers to compare and test the role of experience/contact in the other-race effect, using various operational definitions of this theoretical construct. The models show that to produce an other-race effect computationally, biased experience or learning must intervene during the process of feature selection. This implicates the critical importance of “developmental” learning in the other-race effect.
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paper_title: Perception and Motivation in Face Recognition: A Critical Review of Theories of the Cross-Race Effect

paper_content:

Although humans possess well-developed face processing expertise, face processing is nevertheless subject to a variety of biases. Perhaps the best known of these biases is the Cross-Race Effect—the tendency to have more accurate recognition for same-race than cross-race faces. The current work reviews the evidence for and provides a critical review of theories of the Cross-Race Effect, including perceptual expertise and social cognitive accounts of the bias. The authors conclude that recent hybrid models of the Cross-Race Effect, which combine elements of both perceptual expertise and social cognitive frameworks, provide an opportunity for theoretical synthesis and advancement not afforded by independent expertise or social cognitive models. Finally, the authors suggest future research directions intended to further develop a comprehensive and integrative understanding of biases in face recognition.
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paper_title: Evidence and a Computational Explanation of Cultural Differences in Facial Expression Recognition

paper_content:

Facial expressions are crucial to human social communication, but the extent to which they are innate and universal versus learned and culture dependent is a subject of debate. Two studies explored the effect of culture and learning on facial expression understanding. In Experiment 1, Japanese and U.S. participants interpreted facial expressions of emotion. Each group was better than the other at classifying facial expressions posed by members of the same culture. In Experiment 2, this reciprocal in-group advantage was reproduced by a neurocomputational model trained in either a Japanese cultural context or an American cultural context. The model demonstrates how each of us, interacting with others in a particular cultural context, learns to recognize a culture-specific facial expression dialect.
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paper_title: THIRTY YEARS OF INVESTIGATING THE OWN-RACE BIAS IN MEMORY FOR FACES A Meta-Analytic Review

paper_content:

The current article reviews the own-race bias (ORB) phenomenon in memory for human faces, the finding that own-race faces are better remembered when compared with memory for faces of another, less familiar race. Data were analyzed from 39 research articles, involving 91 independent samples and nearly 5,000 participants. Measures of hit and false alarm rates, and aggregate measures of discriminatio n accuracy and response criterion were examined, including an analysis of 8 study moderators. Several theoretical relationships were also assessed (i.e., the influence of racial attitudes and interracial contact). Overall, results indicated a "mirror effect" pattern in which own-race faces yielded a higher proportion of hits and a lower proportion of false alarms compared with other-race faces. Consistent with this effect, a significant ORB was also found in aggregate measures of discriminatio n accuracy and response criterion. The influence of perceptual learning and differentiation processes in the ORB are discussed, in addition to the practical implications of this phenomenon. She based her identification on Smith's eyes, which she said were greenish-blue and upon his hands which she said were "light and slender" like the holdup man's. Mrs. McCormick testified that Smith's eyes were "different from most colored people ... bright and piercing." Smith's defense attorneys then attempted to parry the state's first thrust in the trial. Mrs. McCormick was handed a picture of a man she couldn't identify. It was a picture of David Charles, with shorter hair, taken while he was in Vietnam. Assistant defense attorney Kitchen asked Mrs. McCormick if she had ever made the statement that all Black people look alike. "Yes, I made that statement," Mrs. McCormick said, "and they do to a certain extent, but there's a difference here" (Lickson, 1974, p. 66). In 1971, five Black men, who became known as the "Quincy Five," were wrongfully indicted for the murder of Khomas Revels during a robbery in Tallahassee, Florida. Although no forensic evidence obtained from the crime scene was ever linked to the men, five White eyewitnesses positively identified them as among the perpetrators. In each of three trials the state argued, "What better evidence can there be than, 'I saw him,' from unprejudiced witnesses? This has been used since time immemorial. This is proof beyond a reasonable doubt. Five eyewitnesses!" (Lickson, 1974, p. 87). Despite the lack of physical evidence against these men, two of the defendants, Dave Roby Keaton and Johnny Frederick, were found guilty on the basis of eyewitness testimony and coerced confessions obtained by investigators . During the third trial involving David
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paper_title: Learning from Imbalanced Data

paper_content:

With the continuous expansion of data availability in many large-scale, complex, and networked systems, such as surveillance, security, Internet, and finance, it becomes critical to advance the fundamental understanding of knowledge discovery and analysis from raw data to support decision-making processes. Although existing knowledge discovery and data engineering techniques have shown great success in many real-world applications, the problem of learning from imbalanced data (the imbalanced learning problem) is a relatively new challenge that has attracted growing attention from both academia and industry. The imbalanced learning problem is concerned with the performance of learning algorithms in the presence of underrepresented data and severe class distribution skews. Due to the inherent complex characteristics of imbalanced data sets, learning from such data requires new understandings, principles, algorithms, and tools to transform vast amounts of raw data efficiently into information and knowledge representation. In this paper, we provide a comprehensive review of the development of research in learning from imbalanced data. Our focus is to provide a critical review of the nature of the problem, the state-of-the-art technologies, and the current assessment metrics used to evaluate learning performance under the imbalanced learning scenario. Furthermore, in order to stimulate future research in this field, we also highlight the major opportunities and challenges, as well as potential important research directions for learning from imbalanced data.
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paper_title: An other-race effect for face recognition algorithms

paper_content:

Psychological research indicates that humans recognize faces of their own race more accurately than faces of other races. This “other-race effect” occurs for algorithms tested in a recent international competition for state-of-the-art face recognition algorithms. We report results for a Western algorithm made by fusing eight algorithms from Western countries and an East Asian algorithm made by fusing five algorithms from East Asian countries. At the low false accept rates required for most security applications, the Western algorithm recognized Caucasian faces more accurately than East Asian faces and the East Asian algorithm recognized East Asian faces more accurately than Caucasian faces. Next, using a test that spanned all false alarm rates, we compared the algorithms with humans of Caucasian and East Asian descent matching face identity in an identical stimulus set. In this case, both algorithms performed better on the Caucasian faces—the “majority” race in the database. The Caucasian face advantage, however, was far larger for the Western algorithm than for the East Asian algorithm. Humans showed the standard other-race effect for these faces, but showed more stable performance than the algorithms over changes in the race of the test faces. State-of-the-art face recognition algorithms, like humans, struggle with “other-race face” recognition.
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paper_title: A multidimensional scaling analysis of own- and cross-race face spaces

paper_content:

Abstract We examined predictions derived from Valentine’s (1991) Multidimensional Space (MDS) framework for own- and other-race face processing. A set of 20 computerized faces was generated from a single prototype. Each face was saved as Black and White, changing only skin tone, such that structurally identical faces were represented in both race categories. Participants made speeded “same-different” judgments to all possible combinations of faces, from which we generated psychological spaces, with “different” RTs as the measure of similarity. Consistent with the MDS framework, all faces were pseudo-normally distributed around the (unseen) prototype. The distribution of faces was consistent with Valentine’s (1991) predictions: despite their physical identity to the White faces, Black faces had lower mean inter-object distances in psychological space. Other-race faces are more densely clustered in psychological space, which could underlie well-known recognition deficits.
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paper_title: Head-and-Face Anthropometric Survey of U.S. Respirator Users

paper_content:

Sizing data generated by the military for use in fitting respirators have been the normative basis for commercial respirator sizing. Anthropometric data developed for males and females of military age in the 1950s and 1960s are still in use today and form the only comprehensive body of information available on this subject. The twofold objective of this study was to: (1) develop an anthropometric database detailing the face size distributions of respirator users using both traditional measurement methods and three-dimensional scanning systems; and (2) use the database to establish fit test panels to be incorporated into the National Institute for Occupational Safety and Health's respirator certification and international standards. A stratified sampling plan was used with three age strata, two gender strata, and four race/ethnic group strata. The plan called for an equal sample size of 166 in each cell. Subjects were obtained at 41 sites from 8 states. In addition to height and weight, 18 facial dimension...
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paper_title: International Anthropometric Study of Facial Morphology in Various Ethnic Groups/Races

paper_content:

When anthropometric methods were introduced into clinical practice to quantify changes in the craniofacial framework, features distinguishing various races/ethnic groups were discovered. To treat congenital or post-traumatic facial disfigurements in members of these groups successfully, surgeons require access to craniofacial databases based on accurate anthropometric measurements. Normative data of facial measurements are indispensable to precise determination of the degree of deviations from the normal. The set of anthropometric measurements of the face in the population studied was gathered by an international team of scientists. Investigators in the country of the given ethnic group, experienced and/or specially trained in anthropometric methods, carried out the measurements. The normal range in each resultant database was then established, providing valuable information about major facial characteristics. Comparison of the ethnic groups' databases with the established norms of the North America whites (NAW) offered the most suitable way to select a method for successful treatment. The study group consisted of 1470 healthy subjects (18 to 30 years), 750 males and 720 females. The largest group (780 subjects, 53.1%) came from Europe, all of them Caucasians. Three were drawn from the Middle-East (180 subjects, 12.2%), five from Asia (300 subjects, 20.4%) and four from peoples of African origin (210 subjects, 14.3%). Their morphological characteristics were determined by 14 anthropometric measurements, 10 of them used already by classic facial artists, Leonardo da Vinci and Albrecht Durer, complemented by four measurements from the nasal, labio-oral and ear regions. In the regions with single measurements, identical values to NAW in forehead height, mouth width, and ear height were found in 99.7% in both sexes, while in those with multiple measurements, vertical measurements revealed a higher frequency of identical values than horizontal ones. The orbital regions exhibited the greatest variations in identical and contrasting measurements in comparison to NAW. Nose heights and widths contrasted sharply: in relation to NAW the nose was very or extremely significantly wide in both sexes of Asian and Black ethnic groups. Among Caucasians, nose height significantly differed from NAW in three ethnic groups, with one shorter and two greater. In the Middle Eastern groups nose width was identical to those of NAW but the height was significantly greater. The present study, conducted by investigators working separately across the world and with small samples of the population, is clearly preliminary in nature and extent. Yet it may fulfill its mission if medical and anthropological investigators continue the work of establishing normative data of the face. These data are urgently needed by medical professionals but have been lacking up till now in western and northern Europe, Asia, and Africa.
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paper_title: Shape analysis of 3D head scan data for U.S. respirator users

paper_content:

In 2003, the National Institute for Occupational Safety and Health (NIOSH) conducted a head-and-face anthropometric survey of diverse, civilian respirator users. Of the 3,997 subjects measured using traditional anthropometric techniques, surface scans and 26 three-dimensional (3D) landmark locations were collected for 947 subjects. The objective of this study was to report the size and shape variation of the survey participants using the 3D data. Generalized Procrustes Analysis (GPA) was conducted to standardize configurations of landmarks associated with individuals into a common coordinate system. The superimposed coordinates for each individual were used as commensurate variables that describe individual shape and were analyzed using Principal Component Analysis (PCA) to identify population variation. The first four principal components (PC) account for 49% of the total sample variation. The first PC indicates that overall size is an important component of facial variability. The second PC accounts for long and narrow or short and wide faces. Longer narrow orbits versus shorter wider orbits can be described by PC3, and PC4 represents variation in the degree of ortho/prognathism. Geometric Morphometrics provides a detailed and interpretable assessment of morphological variation that may be useful in assessing respirators and devising new test and certification standards.
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paper_title: A systematic review of interethnic variability in facial dimensions.

paper_content:

BACKGROUND ::: The earliest recorded facial proportional analysis is in the Greek neoclassical canons (c. 450 b.c.). In contemporary times, there has not yet been a study that describes the relative differences in facial proportions among the world's different ethnic groups. The specific aim of this project was to perform a systematic review of data from the existing literature to evaluate the degree of variability in the facial dimensions among various ethnic groups. ::: ::: ::: METHODS ::: A PubMed database review identified primary articles containing measurements of facial proportions from various ethnic groups. These facial measurements included the heights and widths of the upper, middle, and lower face, which are the features originally described by the neoclassical canons. Coefficients of variation were calculated to derive a unit-free comparison of the degree of variability among different ethnic groups in each of the neoclassically measured facial dimensions. ::: ::: ::: RESULTS ::: The authors' literature search identified 239 potential articles. After screening for the inclusion and exclusion criteria, seven relevant articles were selected. These articles contained data on 11 linear facial measurements from 2359 male and female individuals from 27 different ethnic groups; features that demonstrated the largest differences among the different ethnic populations were forehead height, interocular distance, and nasal width. ::: ::: ::: CONCLUSIONS ::: The greatest interethnic variability in facial proportions exists in the height of the forehead. More pronounced differences among the ethnic groups are also present in the measurements of the eyes, nose, and mouth. There is no significant difference between sexes in the neoclassical facial proportions.
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paper_title: Cultural Confusions Show that Facial Expressions Are Not Universal

paper_content:

Central to all human interaction is the mutual understanding of emotions, achieved primarily by a set of biologically rooted social signals evolved for this purpose-facial expressions of emotion. Although facial expressions are widely considered to be the universal language of emotion, some negative facial expressions consistently elicit lower recognition levels among Eastern compared to Western groups (see [4] for a meta-analysis and [5, 6] for review). Here, focusing on the decoding of facial expression signals, we merge behavioral and computational analyses with novel spatiotemporal analyses of eye movements, showing that Eastern observers use a culture-specific decoding strategy that is inadequate to reliably distinguish universal facial expressions of "fear" and "disgust." Rather than distributing their fixations evenly across the face as Westerners do, Eastern observers persistently fixate the eye region. Using a model information sampler, we demonstrate that by persistently fixating the eyes, Eastern observers sample ambiguous information, thus causing significant confusion. Our results question the universality of human facial expressions of emotion, highlighting their true complexity, with critical consequences for cross-cultural communication and globalization.
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paper_title: THE CHINESE FACE: A 3D ANTHROPOMETRIC ANALYSIS

paper_content:

Traditional facial anthropometry has a long history yet few studies have been conducted using the latest 3D scanning technology. The majority of these facial studies have focused on Caucasian populations. The recent SizeChina survey has collected high resolution 3D data from over 2000 adult Chinese subjects. The 3D facial data of 772 females from the SizeChina database were analyzed in this study. This data was pre-processed to eliminate scan noise, fill holes caused by shadow effect and aligned to a common plane. Thirty one facial landmarks were used for statistical analysis and to create a 3D anthropometric face model. Simple statistics results demonstrated that Chinese females in general have wider and shorter faces compared to other cultures. Correlation analysis showed the breadth dimensions are highly correlated and it seems that heavier people have larger bigonial breadth. Results from the principal component analysis on anthropometric face model reveals that major variations of face shapes are related to face breadth, face depth, face length, chin and jaw position. The results of this study have significant implications for the design of facial products for Chinese women.
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paper_title: Validity and Reliability of Craniofacial Anthropometric Measurement of 3D Digital Photogrammetric Images

paper_content:

Abstract Background: Direct anthropometry performed during a patient examination is the standard technique for quantifying craniofacial dysmorphology, as well as for surgical planning and outcome assessment. Several new technologies have been designed to computerize anthropometric measurements, including three-dimensional (3D) digital photogrammetry. These digital systems have the advantage of acquiring patient craniofacial surface images quickly and noninvasively. Before morphometry using digital photogrammetry can be applied in clinical and research practice, it must be assessed against direct anthropometry. Objective: To evaluate the validity and reliability of facial anthropometric linear distances imaged by 3D digital photogrammetry with respect to direct anthropometry. Design, Setting, Participants, Measures: Standard craniofacial distances were directly measured twice on 20 normal adult volunteers. Craniofacial surfaces were also imaged using the 3dMDface digital photogrammetry system, and distance...
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paper_title: A comparison between Chinese and Caucasian head shapes.

paper_content:

Univariate anthropometric data have long documented a difference in head shape proportion between Chinese and Caucasian populations. This difference has made it impossible to create eyewear, helmets and facemasks that fit both groups well. However, it has been unknown to what extend and precisely how the two populations differ from each other in form. In this study, we applied geometric morphometrics to dense surface data to quantify and characterize the shape differences using a large data set from two recent 3D anthropometric surveys, one in North America and Europe, and one in China. The comparison showed the significant variations between head shapes of the two groups and results demonstrated that Chinese heads were rounder than Caucasian counterparts, with a flatter back and forehead. The quantitative measurements and analyses of these shape differences may be applied in many fields, including anthropometrics, product design, cranial surgery and cranial therapy.
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paper_title: Learning a Dictionary of Shape-Components in Visual Cortex: Comparison with Neurons, Humans and Machines

paper_content:

In this thesis, I describe a quantitative model that accounts for the circuits and computations of the feedforward path of the ventral stream of visual cortex. This model is consistent with a general theory of visual processing that extends the hierarchical model of [Hubel and Wiesel, 1959] from primary to extrastriate visual areas. It attempts to explain the first few hundred milliseconds of visual processing and "immediate recognition". One of the key elements in the approach is the learning of a generic dictionary of shape-components from V2 to IT, which provides an invariant representation to task-specific categorization circuits in higher brain areas. This vocabulary of shape-tuned units is learned in an unsupervised manner from natural images, and constitutes a large and redundant set of image features with different complexities and invariances. This theory significantly extends an earlier approach by [Riesenhuber and Poggio, 1999a] and builds upon several existing neurobiological models and conceptual proposals. ::: First, I present evidence to show that the model can duplicate the tuning properties of neurons in various brain areas (e.g., V1, V4 and IT). In particular, the model agrees with data from V4 about the response of neurons to combinations of simple two-bar stimuli [Reynolds et al., 1999] (within the receptive field of the S2 units) and some of the C2 units in the model show a tuning for boundary conformations which is consistent with recordings from V4 [Pasupathy and Connor, 2001]. Second, I show that not only can the model duplicate the tuning properties of neurons in various brain areas when probed with artificial stimuli, but it can also handle the recognition of objects in the real-world, to the extent of competing with the best computer vision systems. Third, I describe a comparison between the performance of the model and the performance of human observers in a rapid animal vs. non-animal recognition task for which recognition is fast and cortical back-projections are likely to be inactive. Results indicate that the model predicts human performance extremely well when the delay between the stimulus and the mask is about 50 ms. This suggests that cortical back-projections may not play a significant role when the time interval is in this range, and the model may therefore provide a satisfactory description of the feedforward path. ::: Taken together, the evidences suggest that we may have the skeleton of a successful theory of visual cortex. In addition, this may be the first time that a neurobiological model, faithful to the physiology and the anatomy of visual cortex, not only competes with some of the best computer vision systems thus providing a realistic alternative to engineered artificial vision systems, but also achieves performance close to that of humans in a categorization task involving complex natural images. (Copies available exclusively from MIT Libraries, Rm. 14-0551, Cambridge, MA 02139-4307. Ph. 617-253-5668; Fax 617-253-1690.)
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paper_title: A neural mechanism of first impressions

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Evaluating social others requires processing complex information. Nevertheless, we can rapidly form an opinion of an individual during an initial encounter. Moreover, people can vary in these opinions, even though the same information is provided. We investigated the brain mechanisms that give rise to the impressions that are formed on meeting a new person. Neuroimaging revealed that responses in the amygdala and the posterior cingulate cortex (PCC) were stronger while encoding social information that was consistent, relative to inconsistent, with subsequent evaluations. In addition, these responses scaled parametrically with the strength of evaluations. These findings provide evidence for encoding differences on the basis of subsequent evaluations, suggesting that the amygdala and PCC are important for forming first impressions.
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paper_title: Implicit race attitudes predict trustworthiness judgments and economic trust decisions

paper_content:

Trust lies at the heart of every social interaction. Each day we face decisions in which we must accurately assess another individual's trustworthiness or risk suffering very real consequences. In a global marketplace of increasing heterogeneity with respect to nationality, race, and multiple other social categories, it is of great value to understand how implicitly held attitudes about group membership may support or undermine social trust and thereby implicitly shape the decisions we make. Recent behavioral and neuroimaging work suggests that a common mechanism may underlie the expression of implicit race bias and evaluations of trustworthiness, although no direct evidence of a connection exists. In two behavioral studies, we investigated the relationship between implicit race attitude (as measured by the Implicit Association Test) and social trust. We demonstrate that race disparity in both an individual's explicit evaluations of trustworthiness and, more crucially, his or her economic decisions to trust is predicted by that person's bias in implicit race attitude. Importantly, this relationship is robust and is independent of the individual's bias in explicit race attitude. These data demonstrate that the extent to which an individual invests in and trusts others with different racial backgrounds is related to the magnitude of that individual's implicit race bias. The core dimension of social trust can be shaped, to some degree, by attitudes that reside outside conscious awareness and intention.
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paper_title: Structural encoding and identification in face processing: ERP evidence for separate mechanisms

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The present study had two aims. The first aim was to explore the possible top-down effect of facerecognition and/or face-identification processes on the formation of structural representation of faces, as indexed by the N170 ERP component. The second aim was to examine possible ERP manifestations of face identification processes as an initial step for assessing their time course and functional neuroanatomy. Identical N170 potentials were elicited by famous and unfamiliar faces in Experiment 1, when both were irrelevant to the task, suggesting that face familiarity does not affect structural encoding processes. Small but significant differences were observed, however, during later-occurring epochs of the ERPs. In Experiment 2 the participants were instructed to count occasionally occurring portraits of famous politicians while rejecting faces of famous people who were not politicians and faces of unfamiliar people. Although an attempt to identify each face was required, no differences were found in the N170 elicited by faces of unfamiliar people and faces of familiar non-politicians. Famous faces, however, elicited a negative potential that was significantly larger than that elicited by unfamiliar faces between about 250 and 500msec from stimulus onset. This negative component was tentatively identified as an N400 analogue elicited by faces. Both the absence of an effect of familiarity on the N170 and the familiarity face-N400 effect were replicated in Experiment 3, in which the participants made speeded button-press responses in each trial, distinguishing among faces of politicians and faces of famous and unfamiliar non-politicians. In addition, ERP components later than the N400 were found to be associated with the speed of the response but not with face familiarity. We concluded that (1) although reflected by the N170, the structural encoding mechanism is not influenced by the face recognition and identification processes, and (2) the negative component modulated by face familiarity is associated with the semantic activity involved in the identification of familiar faces.
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paper_title: The Fusiform Face Area is Part of a Network that Processes Faces at the Individual Level

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According to modular models of cortical organization, many areas of the extrastriate cortex are dedicated to object categories. These models often assume an early processing stage for the detection of category membership. Can functional imaging isolate areas responsible for detection of members of a category, such as faces or letters? We consider whether responses in three different areas (two selective for faces and one selective for letters) support category detection. Activity in these areas habituates to the repeated presentation of one exemplar more than to the presentation of different exemplars of the same category, but only for the category for which the area is selective. Thus, these areas appear to play computational roles more complex than detection, processing stimuli at the individual level. Drawing from prior work, we suggest that face-selective areas may be involved in the perception of faces at the individual level, whereas letter-selective regions may be tuning themselves to font information in order to recognize letters more efficiently.
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paper_title: The effects of skin tone on race-related amygdala activity: an fMRI investigation

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Previous work has shown differential amygdala response to African-American faces by Caucasian individuals. Furthermore, behavioral studies have demonstrated the existence of skin tone bias, the tendency to prefer light skin to dark skin. In the present study, we used functional magnetic resonance imaging (fMRI) to investigate whether skin tone bias moderates differential race-related amygdala activity. Eleven White participants viewed photographs of unfamiliar Black and White faces with varied skin tone (light, dark). Replicating past research, greater amygdala activity was observed for Black faces than White faces. Furthermore, dark-skinned targets elicited more amygdala activity than light-skinned targets. However, these results were qualified by a significant interaction between race and skin tone, such that amygdala activity was observed at equivalent levels for light- and dark-skinned Black targets, but dark-skinned White targets elicited greater amygdala activity than light-skinned White targets.
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paper_title: An fMRI investigation of race-related amygdala activity in African-American and Caucasian-American individuals

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Functional magnetic resonance imaging (fMRI) was used to examine the nature of amygdala sensitivity to race. Both African-American and Caucasian-American individuals showed greater amygdala activity to African-American targets than to Caucasian-American targets, suggesting that race-related amygdala activity may result from cultural learning rather than from the novelty of other races. Additionally, verbal encoding of African-American targets produced significantly less amygdala activity than perceptual encoding of African-American targets.
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paper_title: The face-sensitive N170 encodes social category information

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Prominent models of face perception posit that the encoding of social categories begins after initial structural encoding has completed. In contrast, we hypothesized that social category encoding may occur simultaneously with structural encoding. While event-related potentials were recorded, participants categorized the sex of sex-typical and sex-atypical faces. Results indicated that the face-sensitive right N170, a component involved in structural encoding, was larger for sex-typical relative to sex-atypical faces. Moreover, its amplitude predicted the efficiency of sex-category judgments. The right P1 component also peaked earlier for sex-typical faces. These findings show that social category encoding and the extraction of lower-level face information operate in parallel, suggesting that they may be accomplished by a single dynamic process rather than two separate mechanisms.
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paper_title: The role of age and ethnic group in face recognition memory: ERP evidence from a combined own-age and own-race bias study

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Abstract Young adult participants are known to more accurately remember faces from both their own age- and ethnic groups. The present study examined combined effects of such own-age and own-race biases by asking young Caucasian participants to learn and remember elderly and young Caucasian as well as elderly and young Asian faces. Neural correlates were assessed by recording event-related potentials (ERPs). Behavioral results indicated both an own-race bias for young but not elderly faces, and an own-age bias for Caucasian but not Asian faces. Importantly, no additional decrease in recognition memory for other-race/other-age faces was detected. An early parietal ERP old/new effect (300–500 ms) was most pronounced for young Caucasian “in-group” faces, while the old/new effect in a later time window (500–800 ms) was generally larger for own- as compared to other-race faces. In conclusion, these findings suggest at least partly different neural processes to accompany the own-race and own-age biases.
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paper_title: Person Categorization and Automatic Racial Stereotyping Effects on Weapon Identification

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Prior stereotyping research provides conflicting evidence regarding the importance of person categorization along a particular dimension for the automatic activation of a stereotype corresponding to that dimension. Experiment 1 replicated a racial stereotyping effect on object identification and examined whether it could be attenuated by encouraging categorization by age. Experiment 2 employed socially complex person stimuli and manipulated whether participants categorized spontaneously or by race. In Experiment 3, the distinctiveness of the racial dimension was manipulated by having Black females appear in the context of either Black males or White females. The results indicated that conditions fostering categorization by race consistently produced automatic racial stereotyping and that conditions fostering nonracial categorization can eliminate automatic racial stereotyping. Implications for the relation between automatic stereotype activation and dimension of categorization are discussed.
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paper_title: Intact performance on an indirect measure of race bias following amygdala damage

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Recent brain imaging and lesion studies provide converging evidence for amygdala involvement in judgments of fear and trust based
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paper_title: Tracking the Timecourse of Social Perception: The Effects of Racial Cues on Event-Related Brain Potentials:

paper_content:

Event-related potentials were used to track social perception processes associated with viewing faces of racial ingroup and outgroup members. Activity associated with three distinct processes was detected. First, peaking at approximately 170 ms, faces were distinguished from nonface stimuli. Second, peaking at approximately 250 ms, ingroup members were differentiated from outgroup members, with a larger component suggesting greater attention to ingroup members. This effect may reflect the spontaneous application of a deeper level of processing to ingroup members. Third, peaking at approximately 520 ms, evaluative differentiation of ingroup and outgroup members occurred, with greater ingroup bias displayed by those with higher levels of prejudice on an explicit measure. Together, the results demonstrate the promise of using neural processes to track the presence, timing, and degree of activation of components relevant to social perception, prejudice, and stereotyping.
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paper_title: Dynamic representations of race: processing goals shape race decoding in the fusiform gyri

paper_content:

People perceive and evaluate others on the basis of social categories, such as race, gender and age. Initial processing of targets in terms of visually salient social categories is often characterized as inevitable. In the current study, we investigated the influence of processing goals on the representation of race in the visual processing stream. Participants were assigned to one of two mixed-race teams and categorized faces according to their group membership or skin color. To assess neural representations of race, we employed multivariate pattern analysis to examined neural activity related to the presentation of Black and White faces. As predicted, patterns of neural activity within the early visual cortex and fusiform gyri (FG) could decode the race of face stimuli above chance and were moderated by processing goals. Race decoding in early visual cortex was above chance in both categorization tasks and below chance in a prefrontal control region. More importantly, race decoding was greater in the FG during the group membership vs skin color categorization task. The results suggest that, ironically, explicit racial categorization can diminish the representation of race in the FG. These findings suggest that representations of race are dynamic, reflecting current processing goals.
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paper_title: The neural correlates of race

paper_content:

Behavioral analyses are a natural choice for understanding the wide-ranging behavioral consequences of racial stereotyping and prejudice. However, studies using neuroimaging and electrophysiological research have recently considered the neural mechanisms that underlie racial categorization and the activation and application of racial stereotypes and prejudice, revealing exciting new insights. Work that we review here points to the importance of neural structures previously associated with face processing, semantic knowledge activation, evaluation and self-regulatory behavioral control, enabling specification of a neural model of race processing. We show how research on the neural correlates of race can serve to link otherwise disparate lines of evidence on the neural underpinnings of a broad array of social-cognitive phenomena; we also consider the implications for effecting change in race relations.
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paper_title: Developmental Intergroup Theory: Explaining and Reducing Children's Social Stereotyping and Prejudice

paper_content:

Social stereotyping and prejudice are intriguing phenomena from the standpoint of theory and, in addition, constitute pressing societal problems. Because stereotyping and prejudice emerge in early childhood, developmental research on causal mechanisms is critical for understanding and controlling stereotyping and prejudice. Such work forms the basis of a new theoretical model, developmental intergroup theory (DIT), which addresses the causal ingredients of stereotyping and prejudice. The work suggests that biases may be largely under environmental control and thus might be shaped via educational, social, and legal policies.
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paper_title: The Categorization-Individuation Model: An Integrative Account of the Other-Race Recognition Deficit.

paper_content:

The other-race effect (ORE), or the finding that same-race faces are better recognized than other-race faces, is one of the best replicated phenomena in face recognition. The current article reviews existing evidence and theory and proposes a new theoretical framework for the ORE, which argues that the effect results from a confluence of social categorization, motivated individuation, and perceptual experience. This categorization-individuation model offers not only a parsimonious account of both classic and recent evidence for category-based biases in face recognition but also links the ORE to broader evidence and theory in social cognition and face perception. Finally, the categorization-individuation model makes a series of novel predictions for how the ORE can be exacerbated, attenuated, or even eliminated via perceptual and motivational processes, both by improving other-race recognition and by reducing same-race recognition. The authors propose that this new model for the ORE also leads to applied interventions that differ sharply from other theories of the ORE, while simultaneously providing an integrative theoretical framework for future research on the ORE.
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paper_title: Facial expressions of emotion are not culturally universal

paper_content:

Since Darwin’s seminal works, the universality of facial expressions of emotion has remained one of the longest standing debates in the biological and social sciences. Briefly stated, the universality hypothesis claims that all humans communicate six basic internal emotional states (happy, surprise, fear, disgust, anger, and sad) using the same facial movements by virtue of their biological and evolutionary origins [Susskind JM, et al. (2008) Nat Neurosci 11:843–850]. Here, we refute this assumed universality. Using a unique computer graphics platform that combines generative grammars [Chomsky N (1965) MIT Press, Cambridge, MA] with visual perception, we accessed the mind’s eye of 30 Western and Eastern culture individuals and reconstructed their mental representations of the six basic facial expressions of emotion. Cross-cultural comparisons of the mental representations challenge universality on two separate counts. First, whereas Westerners represent each of the six basic emotions with a distinct set of facial movements common to the group, Easterners do not. Second, Easterners represent emotional intensity with distinctive dynamic eye activity. By refuting the long-standing universality hypothesis, our data highlight the powerful influence of culture on shaping basic behaviors once considered biologically hardwired. Consequently, our data open a unique nature–nurture debate across broad fields from evolutionary psychology and social neuroscience to social networking via digital avatars.
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paper_title: Differential responses in the fusiform region to same-race and other-race faces

paper_content:

Many studies have shown that people remember faces of their own race better than faces of other races. We investigated the neural substrates of same-race memory superiority using functional MRI (fMRI). European-American (EA) and African-American (AA) males underwent fMRI while they viewed photographs of AA males, EA males and objects under intentional encoding conditions. Recognition memory was superior for same-race versus other-race faces. Individually defined areas in the fusiform region that responded preferentially to faces had greater response to same-race versus other-race faces. Across both groups, memory differences between same-race and other-race faces correlated with activation in left fusiform cortex and right parahippocampal and hippocampal areas. These results suggest that differential activation in fusiform regions contributes to same-race memory superiority.
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paper_title: The neuroscience of race

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The authors review recent research examining how social categories of race and ethnicity are processed, evaluated and incorporated in decision-making. They also speculate about the potential of future work to inform how we recognize and respond to variations in race and its influence on unintended race-based attitudes and decisions.
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paper_title: A Developmental Intergroup Theory of Social Stereotypes and Prejudice

paper_content:

Publisher Summary This chapter proposes a new theory of the formation of social stereotypes and prejudice among children: developmental intergroup theory (DIT). It defines the terms “stereotyping” and “prejudice” to specify the phenomena that the theory is designed to address and outlines the importance of considering the ontogenetic emergence of stereotyping and prejudice. The core characteristics and goals of the theory are described and the key components of intergroup and cognitive-developmental theories that provide foundations for the theoretical model are highlighted. The chapter deals with the mechanisms that hypothesize account for the formation of stereotypes and prejudice among children as a group, emphasizing how the theory differs from other major theories of stereotyping and prejudice. It reviews the environmental, developmental, and individual differences that contribute to variations in stereotyping and prejudice across children. The chapter also discusses the implications of developmental intergroup theory both for research and social action to reduce stereotyping and prejudice.
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paper_title: Social Categorization and Stereotyping: How Social Categorization Biases Person Perception and Face Memory

paper_content:

The current article offers a brief history of research and theory related to social categorization and stereotyping. We begin by outlining research focusing on category selection, category activation, and category application. We then discuss the consequences of social categorization, noting that one of the most important and ubiquitous effects of social categorization is homogenization or assimilation. We then extend this research related to the assimilative consequences of social categorization to the realm of face memory. Specifically, we introduce the Categorization-Individuation Model (see also Hugenberg et al., forthcoming) as a new model of the Cross-Race Effect, or the difficulty many perceivers have in recognizing members of other racial groups. The Categorization-Individuation Model argues that the Cross-Race Effect is due to the tendency to categorize out-group members but individuate in-group members. Finally, we summarize the extant research in support of this new model of person memory and close by discussing the current state of, and potential future directions for, social categorization research. Whereas attitudes may be social psychology’s most indispensable concept (Allport, 1935), social categorization may run a close second. To a great extent, social categorization is one of the cornerstones of the social cognitive revolution in social psychology. The idea that we spontaneously and effortlessly group social targets into categories, and mentally represent and activate beliefs about those categories, is an old one in social psychology (Allport, 1954; Bruner, 1957; Tajfel & Wilkes, 1963). Indeed, the processing efficiency gleaned from such categorization lies at the heart of classic social cognitive theory on person perception (Brewer, 1988; Fiske & Neuberg, 1990; Fiske & Taylor, 1991). Activating and applying social category information, or stereotyping, allows us to navigate a complex social environment by simplifying judgments and conserving cognitive capacity (Macrae, Milne, & Bodenhausen, 1994). In most social situations, we do not need perfect information about others around us. Instead, we
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paper_title: Applying the attractor field model to social cognition: Perceptual discrimination is facilitated, but memory is impaired for faces displaying evaluatively congruent expressions.

paper_content:

A new model of mental representation is applied to social cognition: the attractor field model. Using the model, the authors predicted and found a perceptual advantage but a memory disadvantage for faces displaying evaluatively congruent expressions. In Experiment 1, participants completed a same/different perceptual discrimination task involving morphed pairs of angry-to-happy Black and White faces. Pairs of faces displaying evaluatively incongruent expressions (i.e., happy Black, angry White) were more likely to be labeled as similar and were less likely to be accurately discriminated from one another than faces displaying evaluatively congruent expressions (i.e., angry Black, happy White). Experiment 2 replicated this finding and showed that objective discriminability of stimuli moderated the impact of attractor field effects on perceptual discrimination accuracy. In Experiment 3, participants completed a recognition task for angry and happy Black and White faces. Consistent with the attractor field model, memory accuracy was better for faces displaying evaluatively incongruent expressions. Theoretical and practical implications of these findings are discussed.
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paper_title: Seeing Race: N170 Responses to Race and Their Relation to Automatic Racial Attitudes and Controlled Processing

paper_content:

We examined the relation between neural activity reflecting early face perception processes and automatic and controlled responses to race. Participants completed a sequential evaluative priming task, in which two-tone images of Black faces, White faces, and cars appeared as primes, followed by target words categorized as pleasant or unpleasant, while encephalography was recorded. Half of these participants were alerted that the task assessed racial prejudice and could reveal their personal bias ("alerted" condition). To assess face perception processes, the N170 component of the ERP was examined. For all participants, stronger automatic pro-White bias was associated with larger N170 amplitudes to Black than White faces. For participants in the alerted condition only, larger N170 amplitudes to Black versus White faces were also associated with less controlled processing on the word categorization task. These findings suggest that preexisting racial attitudes affect early face processing and that situational factors moderate the link between early face processing and behavior.
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paper_title: The N170: Understanding the Time Course of Face Perception in the Human Brain

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This chapter reviews the contribution of electromagnetic measures, mostly event-related potentials (ERPs), to our understanding of the time course of face processing in the normal adult brain, with a focus on the 100–200 ms time window after stimulus onset, that is, during during the occipitotemporal component termed the N170. It fi rst describes the N170 component, how it can be defi ned, and its relationship to the vertex positive potential (VPP) response to faces that was reported prior to the N170 in the literature. It then addresses the question of the origin of the largest N170 to faces in terms of electroencephalographic (EEG) signal, neural sources, and functional processes that lead to this effect. It also discusses the controversial issue of whether the N170 refl ects underlying processes that can be at least partly recruited for processing nonface objects following extensive visual experience with these objects. The chapter summarizes the evidence showing that the N170 refl ects both the initial basic-level categorization of the stimulus as a face through the activation of neural face representations and the coding of individual face representations. It then briefl y discusses why the N170 may be a critical time window for other types of face categorizations before summarizing the chapter and addressing the question of how the N170 can be taken as a tool to clarify the dynamics and the nature of early face processes in future research.
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paper_title: Cultural Confusions Show that Facial Expressions Are Not Universal

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Central to all human interaction is the mutual understanding of emotions, achieved primarily by a set of biologically rooted social signals evolved for this purpose-facial expressions of emotion. Although facial expressions are widely considered to be the universal language of emotion, some negative facial expressions consistently elicit lower recognition levels among Eastern compared to Western groups (see [4] for a meta-analysis and [5, 6] for review). Here, focusing on the decoding of facial expression signals, we merge behavioral and computational analyses with novel spatiotemporal analyses of eye movements, showing that Eastern observers use a culture-specific decoding strategy that is inadequate to reliably distinguish universal facial expressions of "fear" and "disgust." Rather than distributing their fixations evenly across the face as Westerners do, Eastern observers persistently fixate the eye region. Using a model information sampler, we demonstrate that by persistently fixating the eyes, Eastern observers sample ambiguous information, thus causing significant confusion. Our results question the universality of human facial expressions of emotion, highlighting their true complexity, with critical consequences for cross-cultural communication and globalization.
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paper_title: Imaging race.

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Researchers have recently begun to use the tools of neuroscience to examine the social psychological responses associated with race. This article serves as a review of the developing literature in this area. It advances the argument that neuroscience studies of race have the potential to shape fundamental assumptions about race, and the interplay between social and biological processes more generally.
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paper_title: Oxford Handbook of Face Perception

paper_content:

Book synopsis: The first ever comprehensive review of the field of face perception, providing a much needed reference work for students and researchers in the brain sciences ::: Includes cutting edge chapters, written and edited by an international team of top researchers ::: Illustrated in full colour throughout ::: The human face is unique among social stimuli in conveying such a variety of different characteristics. A person's identity, sex, race, age, emotional state, focus of attention, facial speech patterns, and attractiveness are all detected and interpreted with relative ease from the face. Humans also display a surprising degree of consistency in the extent to which personality traits, such as trustworthiness and likeability, are attributed to faces. In the past thirty years, face perception has become an area of major interest within psychology, with a rapidly expanding research base. Yet until now, there has been no comprehensive reference work bringing together this ever growing body of research. ::: ::: The Oxford Handbook of Face Perception is the most comprehensive and commanding review of the field ever published. It looks at the functional and neural mechanisms underlying the perception, representation, and interpretation of facial characteristics, such as identity, expression, eye gaze, attractiveness, personality, and race. It examines the development of these processes, their neural correlates in both human and non-human primates, congenital and acquired disorders resulting from their breakdown, and the theoretical and computational frameworks for their underlying mechanisms. With chapters by an international team of leading authorities from the brain sciences, the book is a landmark publication on face perception.
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paper_title: Differential response in the human amygdala to racial outgroup vs ingroup face stimuli.

paper_content:

Here we describe response in the human amygdala to the presentation of racial outgroup vs ingroup faces. Functional magnetic resonance imaging (fMRI) measures of brain activity were acquired while subjects who identified themselves as White or Black viewed photographs of both White and Black faces. Across all subjects, we observed significantly greater blood oxygen-level-dependent (BOLD) signal in the amygdala to outgroup vs ingroup faces, but only during later stimulus presentations. A region of interest (ROI)-based analysis of these voxels revealed a significant interaction between amygdala response to outgroup and ingroup faces over time. Specifically, the greater amygdala activation to outgroup faces during later stimulus presentations was the result of amygdala response habituation to repeated presentations of ingroup faces with sustained responses to outgroup faces. The present results suggest that amygdala responses to human face stimuli are affected by the relationship between the perceived race of the stimulus face and that of the subject. Results are discussed as consistent with a role for the amygdala in encoding socially and/ or biologically relevant information. We conclude that researchers seeking to study brain responses to face stimuli in human subjects should consider the relationship between the race of subjects and stimuli as a significant potential source of variance. Moreover, these data provide a foundation for future related studies in the neuroscience of social cognition and race. NeuroReport 11:2351‐2355 & 2000 Lippincott Williams & Wilkins.
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paper_title: Performance on Indirect Measures of Race Evaluation Predicts Amygdala Activation

paper_content:

We used fMRI to explore the neural substrates involved in the unconscious evaluation of Black and White social groups. Specifically, we focused on the amygdala, a subcortical structure known to play a role in emotional learning and evaluation. In Experiment 1, White American subjects observed faces of unfamiliar Black and White males. The strength of amygdala activation to Black-versus-White faces was correlated with two indirect (unconscious) measures of race evaluation (Implicit Association Test lIATr and potentiated startle), but not with the direct (conscious) expression of race attitudes. In Experiment 2, these patterns were not obtained when the stimulus faces belonged to familiar and positively regarded Black and White individuals. Together, these results suggest that amygdala and behavioral responses to Black-versus-White faces in White subjects reflect cultural evaluations of social groups modified by individual experience.
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paper_title: Perspectives on Racial Phenotypicality Bias

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This article reviews research examining racial phenotypicality bias—within-category stereotypes, prejudice, and discrimination based on race-related phenotypic characteristics of the face. A literature review of research examining skin tone bias, drawing largely from work examining perceptions of Blacks in the United States, reveals that individuals with features typical of members of their racial category are perceived and treated more negatively by social perceivers. Furthermore, this treatment has broad implications for social status and health. Despite this evidence, the tendency to attend to and use within-race variation in phenotypic appearance has been over-looked in social psychological models of impression formation. However, several theoretical frameworks have recently been proposed to explain the role of phenotype-based expectancies in social representation and judgment. Drawing on the strengths of each perspective, a rudimentary model of racial phenotypicality bias is proposed. This analysis s...
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paper_title: Visual features of intermediate complexity and their use in classification

paper_content:

The human visual system analyzes shapes and objects in a series of stages in which stimulus features of increasing complexity are extracted and analyzed. The first stages use simple local features, and the image is subsequently represented in terms of larger and more complex features. These include features of intermediate complexity and partial object views. The nature and use of these higher-order representations remains an open question in the study of visual processing by the primate cortex. Here we show that intermediate complexity (IC) features are optimal for the basic visual task of classification. Moderately complex features are more informative for classification than very simple or very complex ones, and so they emerge naturally by the simple coding principle of information maximization with respect to a class of images. Our findings suggest a specific role for IC features in visual processing and a principle for their extraction.
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paper_title: “We All Look the Same to Me” Positive Emotions Eliminate the Own-Race Bias in Face Recognition

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Extrapolating from the broaden-and-build theory, we hypothesized that positive emotion may reduce the own-race bias in facial recognition. In Experiments 1 and 2, Caucasian participants (N = 89) viewed Black and White faces for a recognition task. They viewed videos eliciting joy, fear, or neutrality before the learning (Experiment 1) or testing (Experiment 2) stages of the task. Results reliably supported the hypothesis. Relative to fear or a neutral state, joy experienced before either stage improved recognition of Black faces and significantly reduced the own-race bias. Discussion centers on possible mechanisms for this reduction of the own-race bias, including improvements in holistic processing and promotion of a common in-group identity due to positive emotions.
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paper_title: Virtual human technology: Capturing sex, race, and age influences in individual pain decision policies

paper_content:

Pain assessment is subject to bias due to characteristics of the individual in pain and of the observing person. Few research studies have examined pain assessment biases in an experimental setting. The present study employs innovative virtual human technology to achieve greater experimental control. A lens model design was used to capture decision-making policies at the idiographic and nomothetic level. Seventy-five undergraduates viewed virtual humans (VH) that varied in sex, race, age, and pain expression. Participants provided computerized ratings with Visual Analogue Scales on the VH's pain intensity, pain unpleasantness, negative mood, coping, and need for medical treatment. Idiographic analyses revealed that individuals used pain expression most frequently as a significant cue. Nomothetic analyses showed that higher pain expression VH and female VH were viewed as having higher pain intensity, higher pain unpleasantness, greater negative mood, worse coping, and a greater need to seek medical treatment than lower pain expression VH and male VH, respectively. Older VH were viewed as having worse coping and a greater need to seek medical treatment than younger VH. This innovative paradigm involving VH technology and a lens model design was shown to be highly effective and could serve as a model for future studies investigating pain-related decision making in healthcare providers.
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paper_title: Can low level image differences account for the ability of human observers to discriminate facial identity

paper_content:

A fundamental difficulty for imageor appearance-based models of face recognition is to distinguish variations in image structure between two different individuals from those that can occur for a given individual due to changes in lighting, facial expression, or pose. The research described in the present article was designed to examine how human observers are able to cope with this problem. In two experiments, observers performed either a match-to-sample task (Experiment 1) or same–different identity judgments (Experiment 2) for photographs of unfamiliar individuals. A key aspect of these studies is that the matching or same stimulus pairs were never identical; that is to say, they always differed in terms of facial expression or the pattern of illumination. In order to provide a quantitative assessment of appearance-based models, we also measured the optical differences for each pair of same or different images using a variety of possible distance metrics based on the pattern of pixel intensities or wavelet decompositions. These difference measures were then correlated with the accuracy of observers’ judgments for each individual stimulus pair. The results clearly show that human observers can readily distinguish relevant from irrelevant image changes in comparisons of facial identity, but that this performance cannot be explained by any of the appearance-based models we tested.
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paper_title: No role for lightness in the perception of black and white? Simultaneous contrast affects perceived skin tone, but not perceived race

paper_content:

Faces of individuals with African and European heritage (henceforth referred to as Black and White respectively) feature two major differences: those of skin tone and morphological characteristics. Although considerations of perceived race are important to various psychological subdisciplines, to date the relative influence of morphological versus photometric characteristics has not been investigated. We attempted to influence the perceived racial typicality of a central target face by manipulating perceived skin tone using the well-known lightness contrast illusion. As expected, ratings of skin tone were influenced by surround faces, yet ratings of perceived racial typicality were not, suggesting a dissociation between the two judgments. Surprisingly, skin tone contributes little to perceived race, leaving facial morphology as the dominant cue. These results may shed light on failures to find effects of racial typicality in studies of prejudice where judgments were based on photographs with altered skin ...
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paper_title: Looking at Faces: First-Order and Second-Order Features as Determinants of Facial Appearance

paper_content:

The encoding and relative importance of first-order (discrete) and second-order (configural) features in mental representations of unfamiliar faces have been investigated. Nonmetric multidimensional scaling (KYST) was carried out on similarity judgments of forty-one photographs of faces (homogeneous with respect to sex, race, facial expression, and, to a lesser extent, age). A large set of ratings, measurements, and ratios of measurements of the faces was regressed against the three-dimensional KYST solution in order to determine the first-order and second-order features used to judge similarity. Parameters characterizing both first-order and second-order features emerged as important determinants of facial similarity. First-order feature parameters characterizing the appearance of the eyes, eyebrows, and mouth, and second-order feature parameters characterizing the position of the eyes, spatial relations between the internal features, and chin shape correlated with the dimensions of the KYST solution. Th...
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paper_title: Race recognition from face images using Weber local descriptor

paper_content:

This paper proposes a race recognition system from face images based on Weber local descriptors (WLD). In the system, first, WLD histogram is extracted from normalized face images. Then Kruskal-Wallis feature selection technique is used to select the best discriminated bins. City block, Euclidean, and chi-square minimum distance classifiers are used for testing. In the experiments, FERET database is used where there are five major race groups: Asian, African or American Black, Hispanic, Middle-Eastern, and White. Experimental results show that the proposed system with WLD histogram, feature selection, and city block distance classifier achieves accuracies of Asian: 97.74%, Black: 96.89%, Hispanic: 92.06%, Middle: 98.33%, and White: 99.53%. These accuracies are significantly higher than those using principal component analyses.
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paper_title: Training Hierarchical Feed-Forward Visual Recognition Models Using Transfer Learning from Pseudo-Tasks

paper_content:

Building visual recognition models that adapt across different domains is a challenging task for computer vision. While feature-learning machines in the form of hierarchial feed-forward models (e.g., convolutional neural networks) showed promise in this direction, they are still difficult to train especially when few training examples are available. In this paper, we present a framework for training hierarchical feed-forward models for visual recognition, using transfer learning from pseudo tasks. These pseudo tasks are automatically constructed from data without supervision and comprise a set of simple pattern-matching operations. We show that these pseudo tasks induce an informative inverse-Wishart prior on the functional behavior of the network, offering an effective way to incorporate useful prior knowledge into the network training. In addition to being extremely simple to implement, and adaptable across different domains with little or no extra tuning, our approach achieves promising results on challenging visual recognition tasks, including object recognition, gender recognition, and ethnicity recognition.
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paper_title: Facial part displacement effect on template-based gender and ethnicity classification

paper_content:

Visual information such as gender, age and ethnicity play critical roles in human identification. Most of gender and ethnicity recognition research works use the full face considering equal discriminant capability for different face parts. In this paper, we improve the gender and ethnicity recognition, by employing the optimum decision making rule on the confidence level of automatically separated face regions using the modified Golden ratio mask. Faces are preprocessed with multiple base point photometric normalization to prevent facial parts displacement in the noted mask, due to different facial parts' distances of people. SVM is employed as the classifier on the extracted Gabor features of each patch to get its confidence level. The final classification results are obtained based on the output of each patch decision using the optimum decision making rule. Finally, using the most accurate normalization approach for each patch, we could achieve 94% and 98% for gender and ethnicity respectively on a dataset composed of FERET and PEAL frontal face images.
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paper_title: Arabic Race Classification of Face Images

paper_content:

The Human face can tell us a lot about a person, their age, gender, expression and feelings, and may be even their race. There is really no substitute for face-to-face social interaction. In this paper, we develop a framework to classify human race from facial images into three categories, (Arabic, Asian, and Caucasian). This is done by transforming the face images to a lower dimension using Principal Component Analysis forwarded to a classifier constructer, using feed foreword neural network (scaled conjugate gradient) with different parameters such as different inputs (PCA components 40, 50, and 60), layers, neurons and training algorithms. The framework achieved high correct classification rate about 83.5 in certain scenarios. The experiments were conducted on a collection of images from FERET database. 160 images for the Asian category (2 images for one subject) and 160 images for the Caucasian category (2 images for one subject), and because there isn’t an Arabic category face image database we constructed our own, 120 images (2 images for one subject). The scaled conjugate gradient achieved about 15% higher than standard back propagation when tested on the conducted experiments. From the experiments the best result was achieved, i.e., in terms of the correct classification rate, time, and epochs needed to train network when the input to the network (PCA components) was 50 components. The framework provided evidence that the Arabic race exists. However, to confirm and further corroborate the findings, the framework needs to be applied on a larger sample.
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paper_title: Face Hallucination: Theory and Practice

paper_content:

In this paper, we study face hallucination, or synthesizing a high-resolution face image from an input low-resolution image, with the help of a large collection of other high-resolution face images. Our theoretical contribution is a two-step statistical modeling approach that integrates both a global parametric model and a local nonparametric model. At the first step, we derive a global linear model to learn the relationship between the high-resolution face images and their smoothed and down-sampled lower resolution ones. At the second step, we model the residue between an original high-resolution image and the reconstructed high-resolution image after applying the learned linear model by a patch-based non-parametric Markov network to capture the high-frequency content. By integrating both global and local models, we can generate photorealistic face images. A practical contribution is a robust warping algorithm to align the low-resolution face images to obtain good hallucination results. The effectiveness of our approach is demonstrated by extensive experiments generating high-quality hallucinated face images from low-resolution input with no manual alignment.
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paper_title: Fuzzy 3D Face Ethnicity Categorization

paper_content:

In this paper, we propose a novel fuzzy 3D face ethnicity categorization algorithm, which contains two stages, learning and mapping. In learning stage, the visual codes are first learned for both the eastern and western individuals using the learned visual codebook (LVC) method, then from these codes we can learn two distance measures, merging distance and mapping distance. Using the merging distance, we can learn the eastern, western and human codes based on the visual codes. In mapping stage, we compute the probabilities for each 3D face mapped to eastern and western individuals using the mapping distance. And the membership degree is determined by our defined membership function. The main contribution of this paper is that we view ethnicity categorization as a fuzzy problem and give an effective solution to assign the 3D face a reasonable membership degree. All experiments are based on the challenging FRGC2.0 3D Face Database. Experimental results illustrate the efficiency and accuracy of our fuzzy 3D face ethnicity categorization method.
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paper_title: Fusing Local Binary Patterns with Wavelet Features for Ethnicity Identification

paper_content:

Ethnicity identification of face images is of interest in many areas of application, but existing methods are few and limited. This paper presents a fusion scheme that uses block-based uniform local binary patterns and Haar wavelet transform to combine local and global features. In particular, the LL subband coefficients of the whole face are fused with the histograms of uniform local binary patterns from block partitions of the face. We applied the principal component analysis on the fused features and managed to reduce the dimensionality of the feature space from 536 down to around 15 without sacrificing too much accuracy. We have conducted a number of preliminary experiments using a collection of 746 subject face images. The test results show good accuracy and demonstrate the potential of fusing global and local features. The fusion approach is robust, making it easy to further improve the identification at both feature and score levels.
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paper_title: Ethnic Features extraction and recognition of human faces

paper_content:

Ethnic Facial Feature is one of the most important face features. We create a face database of ethnic groups and extract facial features by using face recognition technology. In the feature extraction method, we adapt the algebra and geometry features from face database. In algebra features, LDA algorithm extracting the algebraic features of human face images is used. The paper also constructs a new face template to extract the geometric features and locates the points of face templates by using Gabor Wavelet. KNN and C5.0 Classifiers are used to learn the train dataset. The result indicates that the average recognition accuracy rates of Tibetan, Uighur and Zhuang ethnic groups can reach 79% by algebraic features and 90.95% by geometry features.
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paper_title: A real time race classification system

paper_content:

This paper presents the progress toward a face detection and race classification system that is robust and works in real-time. We address the race classification problem as classifying a frontal face into Asian or non-Asian. Firstly, we propose principal component analysis (PCA) for feature generation and independent component analysis (ICA) for feature extraction. Then, we use SVM for training process and combine different SVM classifiers to some new classifiers, which improve the classification rate to a new level. Experiments show that our system achieves a classification rate of 82.5 % based on a database containing 750 face images from FERET.
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paper_title: Spiking neural networks based cortex like mechanism: A case study for facial expression recognition

paper_content:

Ongoing efforts within neuroscience and intelligent system have been directed toward the building of artificial computational models using simulated neuron units as basic building blocks. Such efforts, inspired in the standard design of traditional neural networks, are limited by the difficulties arising from single functional performance and computational inconvenience, especially when modeling large scale, complex and dynamic processes such as cognitive recognition. Here, we show that there is a different form of implementing cortex-like mechanism, the motivation comes directly from recent pioneering works on detailed functional decomposition analysis of the visual cortex and developments on spiking neural networks (SNNs), a promising direction for neural networks, as they utilize information representation as trains of spikes, embedded with spatiotemporal characteristics. A practical implementation is presented, which can be simply described as cortical-like feed-forward hierarchy using biologically plausible neural system. As a proof of principle, a prototype model has been testified on the platform of several facial expression dataset. Of note, small structure modifications and different learning schemes allow for implementing more complicated decision system, showing great potential for discovering implicit pattern of interest and further analysis. Our results support the approach of using such hierarchical consortia as an efficient way of complex pattern analysis task not easily solvable using traditional, single functional way of implementations.
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paper_title: Perceiving the sex and race of faces: the role of shape and colour

paper_content:

Theories of object recognition have emphasized the information conveyed by shape information, whereas theories of face recognition have emphasized properties of superficial features. In the experiments reported here we used novel technology to investigate the relative contributions of shape and superficial colour information to simple categorization decisions about the sex and ‘race’ of faces. The results show that both shape and colour provide useful information for these decisions; shape information was particularly useful for race decisions while colour dominated sex decisions. When both sources of information were combined, the dominant source depended on viewpoint, with angled views emphasizing the contribution of shape and the full-face view colour. The results are discussed within the context of theories of face recognition and their implications for telecommunication applications are considered.
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paper_title: A robust approach to facial ethnicity classification on large scale face databases

paper_content:

In this paper, we propose a novel approach using Kernel Class-dependent Feature Analysis (KCFA) combined with facial color based features to tackle the problem of ethnicity classification on large scale face databases. In our approach, a new design of multiple filtered responses of the Kernel Class-dependent Feature Analysis is used for ethnicity classes. In order to improve the robustness of our system, the facial color based features are also employed to incorporate with the filtered responses to extract the stable ethnicity features. Compared to previous approaches, our proposed method achieves the best accuracy of ethnicity classification on large industrial based face databases and the standard MBGC face database.
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paper_title: A New Automatic Recognition System of Gender, Age and Ethnicity

paper_content:

In this paper a novel approach for recognizing the gender, ethnicity and age with facial images is proposed. The approach is a novel combination of Gabor filter, Adaboost learning and SVM classifier. Gabor filter banks and Adaboost learning are combined to extract key facial features of each pattern. Then use the Gabor+Adaboost features based SVM classifier to recognize the face image of each pattern. The experiment results in the system based on this approach are reported to show a good performance.
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paper_title: On a taxonomy of facial features

paper_content:

After nearly a decade of intensive research in face recognition, no standard organization exists for grouping the salient information available in 2D face images into feature categories. At the same time, human verification of a subject's identity based on facial images lacks a consistent methodology. In this paper we propose a taxonomy of available facial features that: (i) serves as a precursor to studies on the individuality of facial features, (ii) follows a similar well established and accepted organization for fingerprint features, and (iii) contains features computable by both machines and humans as well as by machines alone. This manuscript is intended as a strawman of an organization of facial features, that would hopefully lead to a standardization of such features. Such a facial feature organization will (i) enable studies on the individuality of facial features, which has important ramifications for the acceptance of expert testimony in legal proceedings for determining the identity of an individual from a facial photograph, and (ii) help standardize the framework of commercial face recognition systems.
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paper_title: Oxford Handbook of Face Perception

paper_content:

Book synopsis: The first ever comprehensive review of the field of face perception, providing a much needed reference work for students and researchers in the brain sciences ::: Includes cutting edge chapters, written and edited by an international team of top researchers ::: Illustrated in full colour throughout ::: The human face is unique among social stimuli in conveying such a variety of different characteristics. A person's identity, sex, race, age, emotional state, focus of attention, facial speech patterns, and attractiveness are all detected and interpreted with relative ease from the face. Humans also display a surprising degree of consistency in the extent to which personality traits, such as trustworthiness and likeability, are attributed to faces. In the past thirty years, face perception has become an area of major interest within psychology, with a rapidly expanding research base. Yet until now, there has been no comprehensive reference work bringing together this ever growing body of research. ::: ::: The Oxford Handbook of Face Perception is the most comprehensive and commanding review of the field ever published. It looks at the functional and neural mechanisms underlying the perception, representation, and interpretation of facial characteristics, such as identity, expression, eye gaze, attractiveness, personality, and race. It examines the development of these processes, their neural correlates in both human and non-human primates, congenital and acquired disorders resulting from their breakdown, and the theoretical and computational frameworks for their underlying mechanisms. With chapters by an international team of leading authorities from the brain sciences, the book is a landmark publication on face perception.
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paper_title: Describable Visual Attributes for Face Verification and Image Search

paper_content:

We introduce the use of describable visual attributes for face verification and image search. Describable visual attributes are labels that can be given to an image to describe its appearance. This paper focuses on images of faces and the attributes used to describe them, although the concepts also apply to other domains. Examples of face attributes include gender, age, jaw shape, nose size, etc. The advantages of an attribute-based representation for vision tasks are manifold: They can be composed to create descriptions at various levels of specificity; they are generalizable, as they can be learned once and then applied to recognize new objects or categories without any further training; and they are efficient, possibly requiring exponentially fewer attributes (and training data) than explicitly naming each category. We show how one can create and label large data sets of real-world images to train classifiers which measure the presence, absence, or degree to which an attribute is expressed in images. These classifiers can then automatically label new images. We demonstrate the current effectiveness-and explore the future potential-of using attributes for face verification and image search via human and computational experiments. Finally, we introduce two new face data sets, named FaceTracer and PubFig, with labeled attributes and identities, respectively.
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paper_title: Gender and Ethnicity Specific Generic Elastic Models from a Single 2D Image for Novel 2D Pose Face Synthesis and Recognition

paper_content:

In this paper, we propose a novel method for generating a realistic 3D human face from a single 2D face image for the purpose of synthesizing new 2D face images at arbitrary poses using gender and ethnicity specific models. We employ the Generic Elastic Model (GEM) approach, which elastically deforms a generic 3D depth-map based on the sparse observations of an input face image in order to estimate the depth of the face image. Particularly, we show that Gender and Ethnicity specific GEMs (GE-GEMs) can approximate the 3D shape of the input face image more accurately, achieving a better generalization of 3D face modeling and reconstruction compared to the original GEM approach. We qualitatively validate our method using publicly available databases by showing each reconstructed 3D shape generated from a single image and new synthesized poses of the same person at arbitrary angles. For quantitative comparisons, we compare our synthesized results against 3D scanned data and also perform face recognition using synthesized images generated from a single enrollment frontal image. We obtain promising results for handling pose and expression changes based on the proposed method.
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paper_title: Skin and Bones: The Contribution of Skin Tone and Facial Structure to Racial Prototypicality Ratings

paper_content:

Previous research reveals that a more ‘African’ appearance has significant social consequences, yielding more negative first impressions and harsher criminal sentencing of Black or White individuals. This study is the first to systematically assess the relative contribution of skin tone and facial metrics to White, Black, and Korean perceivers’ ratings of the racial prototypicality of faces from the same three groups. Our results revealed that the relative contribution of metrics and skin tone depended on both perceiver race and face race. White perceivers’ racial prototypicality ratings were less responsive to variations in skin tone than were Black or Korean perceivers’ ratings. White perceivers ratings’ also were more responsive to facial metrics than to skin tone, while the reverse was true for Black perceivers. Additionally, across all perceiver groups, skin tone had a more consistent impact than metrics on racial prototypicality ratings of White faces, with the reverse for Korean faces. For Black faces, the relative impact varied with perceiver race: skin tone had a more consistent impact than metrics for Black and Korean perceivers, with the reverse for White perceivers. These results have significant implications for predicting who will experience racial prototypicality biases and from whom.
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paper_title: Simulating the "other-Race Effect" as a Problem in Perceptual Learning

paper_content:

Abstract We report a series of simulations on the well-known ‘other-race effect’. We trained an autoassociative network on a majority and a minority race of faces, and tested the model's ability to process faces from the two races in different ways. First, the model was better able to reconstruct unlearned majority faces than minority faces. Secondly, the average inter-face similarity was higher for the reconstructed minority faces than for reconstructed majority faces, indicating that the model was coding the majority faces more distinctively than the minority faces. These results held for Caucasian faces as the majority race and Japanese faces as the minority race and vice versa. Thirdly, we simulated a recognition task for same- and other-race faces by using a face history matrix and a recognition task matrix with equal numbers of Caucasian and Japanese faces, and reconstructing these faces as a weighted combination of the two matrices. Using Caucasian faces as the majority race, the model was better a...
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paper_title: POOF: Part-Based One-vs.-One Features for Fine-Grained Categorization, Face Verification, and Attribute Estimation

paper_content:

From a set of images in a particular domain, labeled with part locations and class, we present a method to automatically learn a large and diverse set of highly discriminative intermediate features that we call Part-based One-vs.-One Features (POOFs). Each of these features specializes in discrimination between two particular classes based on the appearance at a particular part. We demonstrate the particular usefulness of these features for fine-grained visual categorization with new state-of-the-art results on bird species identification using the Caltech UCSD Birds (CUB) dataset and parity with the best existing results in face verification on the Labeled Faces in the Wild (LFW) dataset. Finally, we demonstrate the particular advantage of POOFs when training data is scarce.
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paper_title: Minimizing Skin Color Differences Does Not Eliminate the Own-Race Recognition Advantage in Infants.

paper_content:

An abundance of experience with own-race faces and limited to no experience with other-race faces has been associated with better recognition memory for own-race faces in infants, children, and adults. This study investigated the developmental origins of this other-race effect (ORE) by examining the role of a salient perceptual property of faces—that of skin color. Six- and 9-month-olds’ recognition memory for own- and other-race faces was examined using infant-controlled habituation and visual-paired comparison at test. Infants were shown own- or other-race faces in color or with skin color cues minimized in grayscale images. Results for the color stimuli replicated previous findings that infants show an ORE in face recognition memory. Results for the grayscale stimuli showed that even when a salient perceptual cue to race, such as skin color information, is minimized, 6- to 9-month-olds, nonetheless, show an ORE in their face recognition memory. Infants’ use of shape-based and configural cues for face recognition is discussed.
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paper_title: Facial ethnicity classification based on boosted local texture and shape descriptions

paper_content:

Ethnicity is a key demographic attribute of human beings and it plays a important role in automatic machine based face analysis, therefore, there has been increasing attention for face based ethnicity classification in recent years. In this paper, we propose a novel method on such an issue by combining both boosted local texture and shape features extracted from 3D face models, in contrast to the existing ones that only depend on 2D facial images. The proposed method makes use of the Oriented Gradient Maps (OGMs) to highlight local geometry as well as texture variations of entire faces, while further learns a compact set of features which are highly related to the ethnicity property for classification. Experiments are comprehensively carried out on the FRGC v2.0 dataset, and the performance is up to 98.3% to distinguish Asians from non-Asians when 80% samples are used in the training set, demonstrating the effectiveness of the proposed method.
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paper_title: Race Classification Based on Facial Features

paper_content:

Human is unique and is an ancestral gift. Biometric recognition system can use race in identifying the human globally with a particular identity. This paper proposes an algorithm for classification of people into three major races namely Caucasoid, Negroid and Mongoloid, which can easily be integrated into any face recognition system. Firstly, the proposed classification method extracts the distinct primary facial feature, skin color model, and the other secondary features such as forehead and lip of the given face to effectively classify the races. Extensive simulations on images obtained from Yale, FERET and other sources exhibit superior performance of the proposed method.
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paper_title: Towards race-related face identification: research on skin color transfer

paper_content:

We developed a novel skin-color transfer algorithm in order to explore the potential information from human racial groups to improve the face identification. We attempt to find a general technique for transferring human skin colors of different races. The idea is to match the intensity values of two images and transfer the entire mood of the source image to the target image. Further enhancement can be achieved in two ways. One is facial multi-region subdivision to limit the color transfer between corresponding regions. The other is to use "region growing" method based on hue values for color transfer in sub-areas, such as lip, where H channel is dominant in determining pixel colors. The technique appears to be quite simple and results turn out to be encouraging. Our new algorithm is analyzed and compared to other existing algorithm, and evaluated through the psychological study on race related identification. The experiment shows that skin color is amongst one of the factors for race identification, but not the dominant one. This is the first preliminary step as an initial screening process towards the race-related face identification.
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paper_title: An other-race effect for face recognition algorithms

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Psychological research indicates that humans recognize faces of their own race more accurately than faces of other races. This “other-race effect” occurs for algorithms tested in a recent international competition for state-of-the-art face recognition algorithms. We report results for a Western algorithm made by fusing eight algorithms from Western countries and an East Asian algorithm made by fusing five algorithms from East Asian countries. At the low false accept rates required for most security applications, the Western algorithm recognized Caucasian faces more accurately than East Asian faces and the East Asian algorithm recognized East Asian faces more accurately than Caucasian faces. Next, using a test that spanned all false alarm rates, we compared the algorithms with humans of Caucasian and East Asian descent matching face identity in an identical stimulus set. In this case, both algorithms performed better on the Caucasian faces—the “majority” race in the database. The Caucasian face advantage, however, was far larger for the Western algorithm than for the East Asian algorithm. Humans showed the standard other-race effect for these faces, but showed more stable performance than the algorithms over changes in the race of the test faces. State-of-the-art face recognition algorithms, like humans, struggle with “other-race face” recognition.
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paper_title: Social Cognition: Thinking Categorically about Others

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In attempting to make sense of other people, perceivers regularly construct and use categorical representations to simplify and streamline the person perception process. Noting the importance of categorical thinking in everyday life, our emphasis in this chapter is on the cognitive dynamics of categorical social perception. In reviewing current research on this topic, three specific issues are addressed: (a) When are social categories activated by perceivers, (b) what are the typical consequences of category activation, and (c) can perceivers control the influence and expression of categorical thinking? Throughout the chapter, we consider how integrative models of cognitive functioning may inform our understanding of categorical social perception.
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paper_title: Facial expressions of emotion are not culturally universal

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Since Darwin’s seminal works, the universality of facial expressions of emotion has remained one of the longest standing debates in the biological and social sciences. Briefly stated, the universality hypothesis claims that all humans communicate six basic internal emotional states (happy, surprise, fear, disgust, anger, and sad) using the same facial movements by virtue of their biological and evolutionary origins [Susskind JM, et al. (2008) Nat Neurosci 11:843–850]. Here, we refute this assumed universality. Using a unique computer graphics platform that combines generative grammars [Chomsky N (1965) MIT Press, Cambridge, MA] with visual perception, we accessed the mind’s eye of 30 Western and Eastern culture individuals and reconstructed their mental representations of the six basic facial expressions of emotion. Cross-cultural comparisons of the mental representations challenge universality on two separate counts. First, whereas Westerners represent each of the six basic emotions with a distinct set of facial movements common to the group, Easterners do not. Second, Easterners represent emotional intensity with distinctive dynamic eye activity. By refuting the long-standing universality hypothesis, our data highlight the powerful influence of culture on shaping basic behaviors once considered biologically hardwired. Consequently, our data open a unique nature–nurture debate across broad fields from evolutionary psychology and social neuroscience to social networking via digital avatars.
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paper_title: Cultural Confusions Show that Facial Expressions Are Not Universal

paper_content:

Central to all human interaction is the mutual understanding of emotions, achieved primarily by a set of biologically rooted social signals evolved for this purpose-facial expressions of emotion. Although facial expressions are widely considered to be the universal language of emotion, some negative facial expressions consistently elicit lower recognition levels among Eastern compared to Western groups (see [4] for a meta-analysis and [5, 6] for review). Here, focusing on the decoding of facial expression signals, we merge behavioral and computational analyses with novel spatiotemporal analyses of eye movements, showing that Eastern observers use a culture-specific decoding strategy that is inadequate to reliably distinguish universal facial expressions of "fear" and "disgust." Rather than distributing their fixations evenly across the face as Westerners do, Eastern observers persistently fixate the eye region. Using a model information sampler, we demonstrate that by persistently fixating the eyes, Eastern observers sample ambiguous information, thus causing significant confusion. Our results question the universality of human facial expressions of emotion, highlighting their true complexity, with critical consequences for cross-cultural communication and globalization.
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paper_title: Racial differences in retinal vessel geometric characteristics: a multiethnic study in healthy Asians.

paper_content:

PURPOSE ::: To investigate potential racial/ethnic differences in retinal vascular geometric parameters in a multiethnic Asian population (Chinese, Malay, and Indian) free of clinical diseases. ::: ::: ::: METHODS ::: A series of retinal vascular parameters were measured from retinal photographs using a computer-assisted program following a standardized protocol. Healthy participants were defined as nonsmokers, the absence of diabetes mellitus, uncontrolled hypertension, obesity, stroke, heart disease, glaucoma, and retinopathy. ::: ::: ::: RESULTS ::: THERE WERE SIGNIFICANT DIFFERENCES IN MEASUREMENTS OF RETINAL VASCULAR CALIBER, TORTUOSITY, AND FRACTAL DIMENSION AMONG THE THREE ETHNIC GROUPS. IN MULTIPLE LINEAR REGRESSION MODEL CONTROLLING FOR AGE, SEX, BODY MASS INDEX, SYSTOLIC BLOOD PRESSURE, CHOLESTEROL, AND GLUCOSE LEVELS, INDIANS HAD THE LARGEST ARTERIOLAR AND VENULAR CALIBERS (ARTERIOLES [SE]: 158.94 μm [1.00]; venules: 228.26 μm [1.53]), followed by Malays (arterioles: 138.31 μm [0.74]; venules: 204.26 μm [1.13]), and then Chinese (arterioles: 131.20 μm [0.84]; venules: 195.09 μm [1.28]). Chinese had the largest arteriolar and venular tortuosity (arterioles [× $${10}^{5}$$]: 7.20 [0.08] VENULES [ $${10}^{5}$$]: 9.09 [0.10]), and venular fractal dimension (1.244 [0.003]). There were no statistically significant differences in other retinal vascular parameters after correcting multiple comparisons by the method of modified false discovery rate. ::: ::: ::: CONCLUSIONS ::: We found that among ethnic groups composed of healthy Chinese, Malay, and Indians, there were statistically significant differences in several retinal parameters. There exist racial influences in retinal vascular parameters and other yet unknown or unmeasured environmental factor or lifestyle habits and genetic variations not related to race that may also contribute to these differences.
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paper_title: Global texture analysis of iris images for ethnic classification

paper_content:

Iris pattern is commonly regarded as a kind of phenotypic feature without relation to the genes. In this paper, we propose a novel ethnic classification method based on the global texture information of iris images. So we would argue that iris texture is race related, and its genetic information is illustrated in coarse scale texture features, rather than preserved in the minute local features of state-of-the-art iris recognition algorithms. In our scheme, a bank of multichannel 2D Gabor filters is used to capture the global texture information and AdaBoost is used to learn a discriminant classification principle from the pool of the candidate feature set. Finally iris images are grouped into two race categories, Asian and non-Asian. Based on the proposed method, we get an encouraging correct classification rate (CCR) of 85.95% on a mixed database containing 3982 iris samples in our experiments.
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paper_title: Ethnic classification based on iris images

paper_content:

Iris texture is commonly thought to be highly discriminative between eyes and stable over individual lifetime, which makes iris particularly suitable for personal identification. However, iris texture also contains more information related to genes, which has been demonstrated by successful use of ethnic and gender classification based on iris. In this paper, we propose a novel ethnic classification method based on supervised codebook optimizing and Locality-constrained Linear Coding (LLC). The optimized codebook is composed of codes which are distinctive or mutual. Iris images from Asian and non-Asian are classified into two classes in experiments. Extensive experimental results show that the proposed method achieves encouraging classification rate and largely improves the ethnic classification performance comparing to existing algorithms.
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paper_title: Predicting ethnicity and gender from iris texture

paper_content:

Previous researchers have reported success in predicting ethnicity and in predicting gender from features of the iris texture. This paper is the first to consider both problems using similar experimental approaches. Contributions of this work include greater accuracy than previous work on predicting ethnicity from iris texture, empirical evidence that suggests that gender prediction is harder than ethnicity prediction, and empirical evidence that ethnicity prediction is more difficult for females than for males.
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paper_title: Race- and sex-related differences in retinal thickness and foveal pit morphology.

paper_content:

PURPOSE ::: To examine sex- and race-associated differences in macular thickness and foveal pit morphology by using spectral-domain optical coherence tomography (SD-OCT). ::: ::: ::: METHODS ::: One hundred eighty eyes of 90 healthy patients (43 women, 47 men) underwent retinal imaging with spectral-domain OCT. The lateral scale of each macular volume scan was corrected for individual differences in axial length by ocular biometry. From these corrected volumes, Early Treatment Diabetic Retinopathy Study (ETDRS) grids of retinal thickness were generated and compared between the groups. Foveal morphology was measured with previously described algorithms. ::: ::: ::: RESULTS ::: Compared with the Caucasians, the Africans and African Americans had reduced central subfield thickness. Central subfield thickness was also reduced in the women compared with the men, although the women also showed significant thinning in parafoveal regions. There was no difference between the sexes in foveal pit morphology; however, the Africans/African Americans had significantly deeper and broader foveal pits than the Caucasians. ::: ::: ::: CONCLUSIONS ::: Previous studies have reported race- and sex-associated differences in macular thickness, and the inference has been that these differences represent similar anatomic features. However, the data on pit morphology collected in the present study reveal an important and significant variation. Between the sexes, the differences are due to global variability in retinal thickness, whereas the variation in thickness observed between the races appears to be driven by differences in foveal pit morphology. These differences have important implications for the use of SD-OCT in detecting and diagnosing retinal disease.
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paper_title: Human perceptual categorization of iris texture patterns

paper_content:

We report on an experiment in which observers were asked to browse a set of 100 iris images and group them into categories based on similarity of overall texture appearance. Results indicate that there is a natural categorization of iris images into a small number of high-level categories, and then also into subcategories. Also, the categorization reflects the Caucasian / Asian ethnicity of the person. This iris texture categorization has potential application in, for example, creating an indexing algorithm to speed search of an iris database and / or determining soft biometric traits of a person.
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paper_title: Artificial Neural Network for Prediction of Ethnicity Based on Iris Texture

paper_content:

Iris is a reliable biometric that is unique, remarkably stable through the life of an individual, and easy to capture. Many applications include verifying the identity of a subject or identifying an unknown individual from a list of possibilities that involve searching a large database. Prediction of ethnicity separates the data into subcategories that will make this search much faster. We have proposed a new approach, namely artificial neural network to predict the ethnicity based on iris texture features. We have achieved the correct classification rate of 93.3% Caucasian/Asian on a person-disjoint set. This result outperforms the previous works using statistical methods on the same database.
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paper_title: Ethnicity Prediction Based on Iris Texture Features

paper_content:

This paper examines the possibility of predicting ethnicity based on iris texture. This is possible if there are similarities of the iris texture of a certain ethnicity, and these similarities differ from ethnicity to ethnicity. This sort of “soft biometric” prediction could be used, for example, to narrow the search of an enrollment database for a match to probe sample. Using an iris image dataset representing 120 persons and 10-fold person-disjoint cross validation, we obtain 91% correct Asian / Caucasian ethnicity classification.
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paper_title: Learning Appearance Primitives of Iris Images for Ethnic Classification

paper_content:

Iris pattern is commonly regarded as a kind of phenotypic feature without relation to genes. In our previous work, we argued that iris texture is race related, and its genetic information is illustrated in coarse scale texture features, rather than preserved in the minute local features of state-of-the-art iris recognition algorithms. In this paper, we propose a novel ethnic classification method based on learning appearance primitives of iris images. So we not only confirm that iris texture is race related, but also try to find out which kinds of iris visual primitives make iris images look different between Asian and non-Asian. In our scheme, we learned a small finite vocabulary of micro-structures, which are called iris-textons, to represent visual primitives of iris images. Then we use iris-texton histogram to capture the difference between iris textures. Finally iris images are grouped into two race categories, Asian and non-Asian, by support vector machine (SVM). Based on the proposed method, we get a higher correct classification rate (CCR) of 91.02% than our previous method on a database containing 2400 iris samples.
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paper_title: Soft biometric classification using periocular region features

paper_content:

With periocular biometrics gaining attention recently, the goal of this paper is to investigate the effectiveness of local appearance features extracted from the periocular region images for soft biometrie classification. We extract gender and ethnicity information from the periocular region images using grayscale pixel intensities and periocular texture computed by Local Binary Patterns as our features and a SVM classifier. Results are presented on the visible spectrum periocular images obtained from the FRGC face dataset. For 4232 periocular images of 404 subjects, we obtain a baseline gender and ethnicity classification accuracy of 93% and 91%, respectively, using 5-fold cross validation. Furthermore, we show that fusion of the soft biometrie information obtained from our classification approach with the texture based periocular recognition approach results in an overall performance improvement.
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paper_title: Useful Features for Human Verification in Near-Infrared Periocular Images

paper_content:

The periocular region is the part of the face immediately surrounding the eye, and researchers have recently begun to investigate how to use the periocular region for recognition. Understanding how humans recognize faces helped computer vision researchers develop algorithms for face recognition. Likewise, understanding how humans analyze periocular images could benefit researchers developing algorithms for periocular recognition. We conducted two experiments to determine how humans analyze periocular images. In these experiments, we presented pairs of images and asked volunteers to determine whether the two images showed eyes from the same subject or from different subjects. In the first experiment, subjects were paired randomly to create different-subject queries. Our volunteers correctly determined the relationship between the two images in 92% of the queries. In the second experiment, we considered multiple factors in forming different-subject pairs; queries were formed from pairs of subjects with the same gender and race, and with similar eye color, makeup, eyelash length, and eye occlusion. In addition, we limited the amount of time volunteers could view a query pair. On this harder experiment, the correct verification rate was 79%. We asked volunteers to describe what features in the images were helpful to them in making their decisions. In both experiments, eyelashes were reported to be the most helpful feature.
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paper_title: A robust approach to facial ethnicity classification on large scale face databases

paper_content:

In this paper, we propose a novel approach using Kernel Class-dependent Feature Analysis (KCFA) combined with facial color based features to tackle the problem of ethnicity classification on large scale face databases. In our approach, a new design of multiple filtered responses of the Kernel Class-dependent Feature Analysis is used for ethnicity classes. In order to improve the robustness of our system, the facial color based features are also employed to incorporate with the filtered responses to extract the stable ethnicity features. Compared to previous approaches, our proposed method achieves the best accuracy of ethnicity classification on large industrial based face databases and the standard MBGC face database.
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paper_title: Investigating useful and distinguishing features around the eyelash region

paper_content:

Traditionally, iris recognition is always about analyzing and extracting features from iris texture. We proposed to investigate regions around eyelashes and extract useful information which helps us to perform ethnic classification. We propose an algorithm which is easy to implement and effective. First, we locate eyelash region by using ASM to model eyelid boundary. Second, we extract local patch around local landmarks. After image processing, we are able to separate eyelashes and extract features from the directions of eyelashes. Those features are descriptive and can be used to train classifiers. Experimental results show our method can successfully perform East-Asian/Caucasian classification up to 93% accuracy, which shows our proposed method is useful and promising.
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paper_title: Facial part displacement effect on template-based gender and ethnicity classification

paper_content:

Visual information such as gender, age and ethnicity play critical roles in human identification. Most of gender and ethnicity recognition research works use the full face considering equal discriminant capability for different face parts. In this paper, we improve the gender and ethnicity recognition, by employing the optimum decision making rule on the confidence level of automatically separated face regions using the modified Golden ratio mask. Faces are preprocessed with multiple base point photometric normalization to prevent facial parts displacement in the noted mask, due to different facial parts' distances of people. SVM is employed as the classifier on the extracted Gabor features of each patch to get its confidence level. The final classification results are obtained based on the output of each patch decision using the optimum decision making rule. Finally, using the most accurate normalization approach for each patch, we could achieve 94% and 98% for gender and ethnicity respectively on a dataset composed of FERET and PEAL frontal face images.
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paper_title: Gender and ethnic classification of face images

paper_content:

The paper considers hybrid classification architectures for gender and ethnic classification of human faces and shows their feasibility using a collection of 3006 face images corresponding to 1009 subjects from the FERET database. The hybrid approach consists of an ensemble of RBF networks and inductive decision trees (DT). Experimental cross validation (CV) results yield on average accuracy rate of (a) 96% on the gender classification task and (b) 94% on the ethnic classification task. The benefits of the hybrid architecture include (i) robustness via query by consensus provided by the ensembles of RBF networks, and (ii) flexible and adaptive thresholds as opposed to ad hoc and hard thresholds provided by using only DT.
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paper_title: A study of large-scale ethnicity estimation with gender and age variations

paper_content:

In this paper we study large-scale ethnicity estimation under variations of age and gender. The biologically-inspired features are applied to ethnicity classification for the first time. Through a large number of experiments on a large database with more than 21,000 face images, we systematically study the effect of gender and age variations on ethnicity estimation. Our finding is that ethnicity classification can have high accuracy in most cases, but an interesting phenomenon is observed that the ethnic classification accuracies could be reduced by 6∼8% in average when female faces are used for training while males for testing. The study results provide a guide for face processing on a multi-ethnic database, e.g., image collection from the Internet, and may inspire further psychological studies on ethnic grouping with gender and age variations. We also apply the methods to the whole MORPH-II database with more than 55,000 face images for ethnicity classification of five races. It is the first time that ethnicity estimation is performed on so large a database.
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paper_title: Joint estimation of age, gender and ethnicity: CCA vs. PLS

paper_content:

Human age, gender and ethnicity are valuable demographic information about a population. These measures are also considered important soft biometric traits for human recognition or search. Usually the three traits are studied separately. A recent study [9] shows that the three traits can be estimated simultaneously based on a multi-label regression formulation. The linear and kernel partial least squares (PLS) models are adopted to solve the multi-label regression problem in [9]. In this study, we investigate the canonical correlation analysis (CCA) based methods, including linear CCA, regularized CCA (rCCA), and kernel CCA (KCCA), and compare to the PLS models in solving the joint estimation problem. Interestingly, we found a consistent ranking of the five methods in estimating age, gender, and ethnicity. More importantly, we found that the CCA based methods can derive an extremely low dimensionality in estimating age, gender and ethnicity, which has not been shown in previous research, to the best of our knowledge. The experiments are conducted on a very large database of more than 55,000 face images.
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paper_title: Fusing Local Binary Patterns with Wavelet Features for Ethnicity Identification

paper_content:

Ethnicity identification of face images is of interest in many areas of application, but existing methods are few and limited. This paper presents a fusion scheme that uses block-based uniform local binary patterns and Haar wavelet transform to combine local and global features. In particular, the LL subband coefficients of the whole face are fused with the histograms of uniform local binary patterns from block partitions of the face. We applied the principal component analysis on the fused features and managed to reduce the dimensionality of the feature space from 536 down to around 15 without sacrificing too much accuracy. We have conducted a number of preliminary experiments using a collection of 746 subject face images. The test results show good accuracy and demonstrate the potential of fusing global and local features. The fusion approach is robust, making it easy to further improve the identification at both feature and score levels.
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paper_title: Where is who: large-scale photo retrieval by facial attributes and canvas layout

paper_content:

The ubiquitous availability of digital cameras has made it easier than ever to capture moments of life, especially the ones accompanied with friends and family. It is generally believed that most family photos are with faces that are sparsely tagged. Therefore, a better solution to manage and search in the tremendously growing personal or group photos is highly anticipated. In this paper, we propose a novel way to search for face photos by simultaneously considering attributes (e.g., gender, age, and race), positions, and sizes of the target faces. To better match the content and layout of the multiple faces in mind, our system allows the user to graphically specify the face positions and sizes on a query "canvas," where each attribute combination is defined as an icon for easier representation. As a secondary feature, the user can even place specific faces from the previous search results for appearance-based retrieval. The scenario has been realized on a tablet device with an intuitive touch interface. Experimenting with a large-scale Flickr dataset of more than 200k faces, the proposed formulation and joint ranking have made us achieve a hit rate of 0.420 at rank 100, significantly improving from 0.036 of the prior search scheme using attributes alone. We have also achieved an average running time of 0.0558 second by the proposed block-based indexing approach.
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paper_title: Ethnicity Identification from Face Images

paper_content:

Human facial images provide the demographic information, such as ethnicity and gender. Conversely, ethnicity and gender also play an important role in face-related applications. Image-based ethnicity identification problem is addressed in a machine learning framework. The Linear Discriminant Analysis (LDA) based scheme is presented for the two-class (Asian vs. non-Asian) ethnicity classification task. Multiscale analysis is applied to the input facial images. An ensemble framework, which integrates the LDA analysis for the input face images at different scales, is proposed to further improve the classification performance. The product rule is used as the combination strategy in the ensemble. Experimental results based on a face database containing 263 subjects (2,630 face images, with equal balance between the two classes) are promising, indicating that LDA and the proposed ensemble framework have sufficient discriminative power for the ethnicity classification problem. The normalized ethnicity classification scores can be helpful in the facial identity recognition. Useful as a "soft" biometric, face matching scores can be updated based on the output of ethnicity classification module. In other words, ethnicity classifier does not have to be perfect to be useful in practice.© (2004) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
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paper_title: Spiking neural networks based cortex like mechanism: A case study for facial expression recognition

paper_content:

Ongoing efforts within neuroscience and intelligent system have been directed toward the building of artificial computational models using simulated neuron units as basic building blocks. Such efforts, inspired in the standard design of traditional neural networks, are limited by the difficulties arising from single functional performance and computational inconvenience, especially when modeling large scale, complex and dynamic processes such as cognitive recognition. Here, we show that there is a different form of implementing cortex-like mechanism, the motivation comes directly from recent pioneering works on detailed functional decomposition analysis of the visual cortex and developments on spiking neural networks (SNNs), a promising direction for neural networks, as they utilize information representation as trains of spikes, embedded with spatiotemporal characteristics. A practical implementation is presented, which can be simply described as cortical-like feed-forward hierarchy using biologically plausible neural system. As a proof of principle, a prototype model has been testified on the platform of several facial expression dataset. Of note, small structure modifications and different learning schemes allow for implementing more complicated decision system, showing great potential for discovering implicit pattern of interest and further analysis. Our results support the approach of using such hierarchical consortia as an efficient way of complex pattern analysis task not easily solvable using traditional, single functional way of implementations.
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paper_title: Gender and ethnic classification of human faces using hybrid classifiers

paper_content:

This paper considers hybrid classification architectures for gender and ethnic classification of human faces and shows their feasibility using a collection of 3006 face images corresponding to 1009 subjects from the FERET database. The hybrid approach consists of an ensemble of RBF networks and inductive decision trees (DT). Experimental cross validation (CV) results yield an average accuracy rate of (a) 96% on the gender classification task and (b) 94% on the ethnic classification task. The benefits of our hybrid architecture include: (i) robustness via query by consensus provided by the ensembles of RBF networks, and (ii) flexible and adaptive thresholds as opposed to ad hoc and hard thresholds provided by using only DT.
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paper_title: 3D Face Recognition Using Isogeodesic Stripes

paper_content:

In this paper, we present a novel approach to 3D face matching that shows high effectiveness in distinguishing facial differences between distinct individuals from differences induced by nonneutral expressions within the same individual. The approach takes into account geometrical information of the 3D face and encodes the relevant information into a compact representation in the form of a graph. Nodes of the graph represent equal width isogeodesic facial stripes. Arcs between pairs of nodes are labeled with descriptors, referred to as 3D Weighted Walkthroughs (3DWWs), that capture the mutual relative spatial displacement between all the pairs of points of the corresponding stripes. Face partitioning into isogeodesic stripes and 3DWWs together provide an approximate representation of local morphology of faces that exhibits smooth variations for changes induced by facial expressions. The graph-based representation permits very efficient matching for face recognition and is also suited to being employed for face identification in very large data sets with the support of appropriate index structures. The method obtained the best ranking at the SHREC 2008 contest for 3D face recognition. We present an extensive comparative evaluation of the performance with the FRGC v2.0 data set and the SHREC08 data set.
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paper_title: 3D Face Discriminant Analysis Using Gauss-Markov Posterior Marginals

paper_content:

We present a Markov Random Field model for the analysis of lattices (e.g., images or 3D meshes) in terms of the discriminative information of their vertices. The proposed method provides a measure field that estimates the probability of each vertex being “discriminative” or “nondiscriminative” for a given classification task. To illustrate the applicability and generality of our framework, we use the estimated probabilities as feature scoring to define compact signatures for three different classification tasks: 1) 3D Face Recognition, 2) 3D Facial Expression Recognition, and 3) Ethnicity-based Subject Retrieval, obtaining very competitive results. The main contribution of this work lies in the development of a novel framework for feature selection in scenaria in which the most discriminative information is smoothly distributed along a lattice.
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paper_title: Distinguishing Facial Features for Ethnicity-Based 3D Face Recognition

paper_content:

Among different approaches for 3D face recognition, solutions based on local facial characteristics are very promising, mainly because they can manage facial expression variations by assigning different weights to different parts of the face. However, so far, a few works have investigated the individual relevance that local features play in 3D face recognition with very simple solutions applied in the practice. In this article, a local approach to 3D face recognition is combined with a feature selection model to study the relative relevance of different regions of the face for the purpose of discriminating between different subjects. The proposed solution is experimented using facial scans of the Face Recognition Grand Challenge dataset. Results of the experimentation are two-fold: they quantitatively demonstrate the assumption that different regions of the face have different relevance for face discrimination and also show that the relevance of facial regions changes for different ethnic groups.
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paper_title: Fuzzy 3D Face Ethnicity Categorization

paper_content:

In this paper, we propose a novel fuzzy 3D face ethnicity categorization algorithm, which contains two stages, learning and mapping. In learning stage, the visual codes are first learned for both the eastern and western individuals using the learned visual codebook (LVC) method, then from these codes we can learn two distance measures, merging distance and mapping distance. Using the merging distance, we can learn the eastern, western and human codes based on the visual codes. In mapping stage, we compute the probabilities for each 3D face mapped to eastern and western individuals using the mapping distance. And the membership degree is determined by our defined membership function. The main contribution of this paper is that we view ethnicity categorization as a fuzzy problem and give an effective solution to assign the 3D face a reasonable membership degree. All experiments are based on the challenging FRGC2.0 3D Face Database. Experimental results illustrate the efficiency and accuracy of our fuzzy 3D face ethnicity categorization method.
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paper_title: Anthropometric 3D Face Recognition

paper_content:

We present a novel anthropometric three dimensional (Anthroface 3D) face recognition algorithm, which is based on a systematically selected set of discriminatory structural characteristics of the human face derived from the existing scientific literature on facial anthropometry. We propose a novel technique for automatically detecting 10 anthropometric facial fiducial points that are associated with these discriminatory anthropometric features. We isolate and employ unique textural and/or structural characteristics of these fiducial points, along with the established anthropometric facial proportions of the human face for detecting them. Lastly, we develop a completely automatic face recognition algorithm that employs facial 3D Euclidean and geodesic distances between these 10 automatically located anthropometric facial fiducial points and a linear discriminant classifier. On a database of 1149 facial images of 118 subjects, we show that the standard deviation of the Euclidean distance of each automatically detected fiducial point from its manually identified position is less than 2.54 mm. We further show that the proposed Anthroface 3D recognition algorithm performs well (equal error rate of 1.98% and a rank 1 recognition rate of 96.8%), out performs three of the existing benchmark 3D face recognition algorithms, and is robust to the observed fiducial point localization errors.
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paper_title: Learning structurally discriminant features in 3D faces

paper_content:

In this paper, we derive a data mining framework to analyze 3D features on human faces. The framework leverages kernel density estimators, genetic algorithm and an information complexity criterion to identify discriminant feature-clusters of lower dimensionality. We apply this framework on human face anthropometry data of 32 features collected from each of the 300 3D face mesh models. The feature-subsets that we infer as the output establishes domain knowledge for the challenging problem of 3D face recognition with dense 3D gallery models and sparse or low resolution probes.
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paper_title: Ethnicity- and Gender-based Subject Retrieval Using 3-D Face-Recognition Techniques

paper_content:

While the retrieval of datasets from human subjects based on demographic characteristics such as gender or race is an ability with wide-ranging application, it remains poorly-studied. In contrast, a large body of work exists in the field of biometrics which has a different goal: the recognition of human subjects. Due to this disparity of interest, existing methods for retrieval based on demographic attributes tend to lag behind the more well-studied algorithms designed purely for face matching. The question this raises is whether a face recognition system could be leveraged to solve these other problems and, if so, how effective it could be. In the current work, we explore the limits of such a system for gender and ethnicity identification given (1) a ground truth of demographically-labeled, textureless 3-D models of human faces and (2) a state-of-the-art face-recognition algorithm. Once trained, our system is capable of classifying the gender and ethnicity of any such model of interest. Experiments are conducted on 4007 facial meshes from the benchmark Face Recognition Grand Challenge v2 dataset.
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paper_title: Gender and Ethnicity Specific Generic Elastic Models from a Single 2D Image for Novel 2D Pose Face Synthesis and Recognition

paper_content:

In this paper, we propose a novel method for generating a realistic 3D human face from a single 2D face image for the purpose of synthesizing new 2D face images at arbitrary poses using gender and ethnicity specific models. We employ the Generic Elastic Model (GEM) approach, which elastically deforms a generic 3D depth-map based on the sparse observations of an input face image in order to estimate the depth of the face image. Particularly, we show that Gender and Ethnicity specific GEMs (GE-GEMs) can approximate the 3D shape of the input face image more accurately, achieving a better generalization of 3D face modeling and reconstruction compared to the original GEM approach. We qualitatively validate our method using publicly available databases by showing each reconstructed 3D shape generated from a single image and new synthesized poses of the same person at arbitrary angles. For quantitative comparisons, we compare our synthesized results against 3D scanned data and also perform face recognition using synthesized images generated from a single enrollment frontal image. We obtain promising results for handling pose and expression changes based on the proposed method.
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paper_title: Facial ethnicity classification based on boosted local texture and shape descriptions

paper_content:

Ethnicity is a key demographic attribute of human beings and it plays a important role in automatic machine based face analysis, therefore, there has been increasing attention for face based ethnicity classification in recent years. In this paper, we propose a novel method on such an issue by combining both boosted local texture and shape features extracted from 3D face models, in contrast to the existing ones that only depend on 2D facial images. The proposed method makes use of the Oriented Gradient Maps (OGMs) to highlight local geometry as well as texture variations of entire faces, while further learns a compact set of features which are highly related to the ethnicity property for classification. Experiments are comprehensively carried out on the FRGC v2.0 dataset, and the performance is up to 98.3% to distinguish Asians from non-Asians when 80% samples are used in the training set, demonstrating the effectiveness of the proposed method.
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paper_title: Multimodal Facial Gender and Ethnicity Identification

paper_content:

Human faces provide demographic information, such as gender and ethnicity. Different modalities of human faces, e.g., range and intensity, provide different cues for gender and ethnicity identifications. In this paper we exploit the range information of human faces for ethnicity identification using a support vector machine. An integration scheme is also proposed for ethnicity and gender identifications by combining the registered range and intensity images. The experiments are conducted on a database containing 1240 facial scans of 376 subjects. It is demonstrated that the range modality provides competitive discriminative power on ethnicity and gender identifications to the intensity modality. For both gender and ethnicity identifications, the proposed integration scheme outperforms each individual modality.
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paper_title: Characterizing human shape variation using 3D anthropometric data

paper_content:

Characterizing the variations of the human body shape is fundamentally important in many applications ranging from animation to product design. 3D scanning technology makes it possible to digitize the complete surfaces of a large number of human bodies, providing much richer information about the body shape than traditional anthropometric measurements. This technology opens up opportunities to extract new measurements for quantifying the body shape. In this paper, we present a new method for extracting the main modes of variations of the human shape from a 3D anthropometric database. Previous approaches rely on anatomical landmarks. Using a volumetric representation, we show that human shape analysis can be performed despite the lack of such information. We first introduce a technique for repairing the 3D models from the original scans. Principal component analysis analysis is then applied to the volumetric description of a set of human models to extract dominant components of shape variability for a target population. We demonstrate a good reconstruction of the original models from a reduced number of components. Finally, we provide tools for visualizing the main modes of human shape variation.
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paper_title: Texas 3D Face Recognition Database

paper_content:

We make the Texas 3D Face Recognition Database available to researchers in three dimensional (3D) face recognition and other related areas. This database contains 1149 pairs of high resolution, pose normalized, preprocessed, and perfectly aligned color and range images of 118 adult human subjects acquired using a stereo camera. The images are accompanied with information about the subjects' gender, ethnicity, facial expression, and the locations of 25 manually located anthropometric facial fiducial points. Specific partitions of the data for developing and evaluating 3D face recognition algorithms are also included.
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paper_title: A unified learning framework for real time face detection and classification

paper_content:

This paper presents progress toward an integrated, robust, real-time face detection and demographic analysis system. Faces are detected and extracted using the fast algorithm proposed by P. Viola and M.J. Jones (2001). Detected faces are passed to a demographic (gender and ethnicity) classifier which uses the same architecture as the face detector. This demographic classifier is extremely fast, and delivers error rates slightly better than the best-known classifiers. To counter the unconstrained and noisy sensing environment, demographic information is integrated across time for each individual. Therefore, the final demographic classification combines estimates from many facial detections in order to reduce the error rate. The entire system processes 10 frames per second on an 800-MHz Intel Pentium III.
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paper_title: Ethnicity classification based on a hierarchical fusion

paper_content:

In this paper, we propose a cascaded multimodal biometrics system involving a fusion of frontal face and lateral gait, for the specific problem of ethnicity classification. This system performs human ethnicity classification first from the cues of gait recorded by a long-distance camera and requires next classification using facial images captured by a short-distance camera only when gait based ethnicity identification fails. For gait, we use Gait Energy Image (GEI), a spatio-temporal compact representation of gait in video, to characterize human walking properties. For face, we extract the well-known Gabor feature to render the effective facial appearance information. Experimental results obtained from a database of 22 subjects containing 12 East-Asian and 10 South-American shows that this cascaded system is capable of providing competitive discriminative power on ethnicity with a correct classification rate over 95%.
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paper_title: Performing content-based retrieval of humans using gait biometrics

paper_content:

In order to analyse surveillance video, we need to efficiently explore large datasets containing videos of walking humans. Effective analysis of such data relies on retrieval of video data which has been enriched using semantic annotations. A manual annotation process is time-consuming and prone to error due to subject bias however, at surveillance-image resolution, the human walk (their gait) can be analysed automatically. We explore the content-based retrieval of videos containing walking subjects, using semantic queries. We evaluate current research in gait biometrics, unique in its effectiveness at recognising people at a distance. We introduce a set of semantic traits discernible by humans at a distance, outlining their psychological validity. Working under the premise that similarity of the chosen gait signature implies similarity of certain semantic traits we perform a set of semantic retrieval experiments using popular Latent Semantic Analysis techniques. We perform experiments on a dataset of 2000 videos of people walking in laboratory conditions and achieve promising retrieval results for features such as Sex (mAP ?=? 14% above random), Age (mAP ?=? 10% above random) and Ethnicity (mAP ?=? 9% above random).
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paper_title: Ethnicity classification based on gait using multi-view fusion

paper_content:

The determination of ethnicity of an individual, as a soft biometrics, can be very useful in a video-based surveillance system. Currently, face is commonly used to determine the ethnicity of a person. Up to now, gait has been used for individual recognition and gender classification but not for ethnicity determination. This paper focuses on the ethnicity determination based on fusion of multi-view gait. Gait Energy Image (GEI) is used to analyze the recognition power of gait for ethnicity. Feature fusion, score fusion and decision fusion from multiple views of gait are explored. For the feature fusion, GEI images and camera views are put together to render a third-order tensor (x; y; view). A multilinear principal component analysis (MPCA) is used to extract features from tensor objects which integrate all views. For the score fusion, the similarity scores measured from single views are combined with a weighted SUM rule. For the decision fusion, ethnicity classification is realized on each individual view first. The classification results are then combined to make the final determination with a majority vote rule. A database of 36 walking people (East Asian and South American) was acquired from 7 different camera views. The experimental results show that ethnicity can be determined from human gait in video automatically. The classification rate is improved by fusing multiple camera views and a comparison among different fusion schemes shows that the MPCA based feature fusion performs the best
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paper_title: A Novel Survey Based on Multiethnic Facial Semantic Web

paper_content:

The face includes a number of facial features which are various in minorities. Firstly, according to the correlations of the face parts shape semantics, multiethnic facial semantic web is proposed. It represents the relationship which belongs to the same minority and the difference of that belongs to the different minorities. Secondly, multiethnic facial semantic web is reduced by the correlations between the parts of the face. The semantic web which is reduced can maintains most available information which is belong to original semantic web, reduces the complexity and indirectly analysis the national facial features. Lastly, the effectiveness of our experiment is demonstrated by some real-word data sets.
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paper_title: Gender and ethnicity identification from silhouetted face profiles

paper_content:

This paper demonstrates, to our best knowledge, the first attempt on gender and ethnicity identification from silhouetted face profiles using a computer vision technique. The results achieved, after testing on 441 images, show that silhouetted face profiles have a lot of information, in particular, for ethnicity identification. Shape context based matching [1] was employed for classification. The test samples were multi-ethnic. Average accuracy for gender was 71.20% and for ethnicity 71.66%. However, the accuracy was significantly higher for some classes, such as 83.41% for females (in case of gender identification) and 80.37% for East and South East Asians (in case of ethnicity identification).
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paper_title: Distinguishing Facial Features for Ethnicity-Based 3D Face Recognition

paper_content:

Among different approaches for 3D face recognition, solutions based on local facial characteristics are very promising, mainly because they can manage facial expression variations by assigning different weights to different parts of the face. However, so far, a few works have investigated the individual relevance that local features play in 3D face recognition with very simple solutions applied in the practice. In this article, a local approach to 3D face recognition is combined with a feature selection model to study the relative relevance of different regions of the face for the purpose of discriminating between different subjects. The proposed solution is experimented using facial scans of the Face Recognition Grand Challenge dataset. Results of the experimentation are two-fold: they quantitatively demonstrate the assumption that different regions of the face have different relevance for face discrimination and also show that the relevance of facial regions changes for different ethnic groups.
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paper_title: Performance Evaluation of Face Recognition Algorithms on the Asian Face Database, KFDB

paper_content:

Human face is one of the most common and useful keys to a person's identity. Many algorithms have been developed for automatic face recognition. And a number of commercial products have reached the market already. In general, however, many believe that the technology has yet to improve further, particularly to overcome the instability due to variable illuminations, expressions, poses and accessories. These variations often lead to large nonlinear variation in facial image. To date it is a very important issue to understand the limitation of the current face recognition technology. In this paper, we report the experimental result of face recognition performed using PCA (Principal Component Analysis), LFA(Local Feature Analysis) and correlation matching algorithms on the KFDB(Korean Face Database) which contains Korean face images taken under various conditions.
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paper_title: Ethnic Features extraction and recognition of human faces

paper_content:

Ethnic Facial Feature is one of the most important face features. We create a face database of ethnic groups and extract facial features by using face recognition technology. In the feature extraction method, we adapt the algebra and geometry features from face database. In algebra features, LDA algorithm extracting the algebraic features of human face images is used. The paper also constructs a new face template to extract the geometric features and locates the points of face templates by using Gabor Wavelet. KNN and C5.0 Classifiers are used to learn the train dataset. The result indicates that the average recognition accuracy rates of Tibetan, Uighur and Zhuang ethnic groups can reach 79% by algebraic features and 90.95% by geometry features.
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paper_title: Spiking neural networks based cortex like mechanism: A case study for facial expression recognition

paper_content:

Ongoing efforts within neuroscience and intelligent system have been directed toward the building of artificial computational models using simulated neuron units as basic building blocks. Such efforts, inspired in the standard design of traditional neural networks, are limited by the difficulties arising from single functional performance and computational inconvenience, especially when modeling large scale, complex and dynamic processes such as cognitive recognition. Here, we show that there is a different form of implementing cortex-like mechanism, the motivation comes directly from recent pioneering works on detailed functional decomposition analysis of the visual cortex and developments on spiking neural networks (SNNs), a promising direction for neural networks, as they utilize information representation as trains of spikes, embedded with spatiotemporal characteristics. A practical implementation is presented, which can be simply described as cortical-like feed-forward hierarchy using biologically plausible neural system. As a proof of principle, a prototype model has been testified on the platform of several facial expression dataset. Of note, small structure modifications and different learning schemes allow for implementing more complicated decision system, showing great potential for discovering implicit pattern of interest and further analysis. Our results support the approach of using such hierarchical consortia as an efficient way of complex pattern analysis task not easily solvable using traditional, single functional way of implementations.
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paper_title: The CAS-PEAL Large-Scale Chinese Face Database and Baseline Evaluations

paper_content:

In this paper, we describe the acquisition and contents of a large-scale Chinese face database: the CAS-PEAL face database. The goals of creating the CAS-PEAL face database include the following: 1) providing the worldwide researchers of face recognition with different sources of variations, particularly pose, expression, accessories, and lighting (PEAL), and exhaustive ground-truth information in one uniform database; 2) advancing the state-of-the-art face recognition technologies aiming at practical applications by using off-the-shelf imaging equipment and by designing normal face variations in the database; and 3) providing a large-scale face database of Mongolian. Currently, the CAS-PEAL face database contains 99 594 images of 1040 individuals (595 males and 445 females). A total of nine cameras are mounted horizontally on an arc arm to simultaneously capture images across different poses. Each subject is asked to look straight ahead, up, and down to obtain 27 images in three shots. Five facial expressions, six accessories, and 15 lighting changes are also included in the database. A selected subset of the database (CAS-PEAL-R1, containing 30 863 images of the 1040 subjects) is available to other researchers now. We discuss the evaluation protocol based on the CAS-PEAL-R1 database and present the performance of four algorithms as a baseline to do the following: 1) elementarily assess the difficulty of the database for face recognition algorithms; 2) preference evaluation results for researchers using the database; and 3) identify the strengths and weaknesses of the commonly used algorithms.
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paper_title: Learning from Imbalanced Data

paper_content:

With the continuous expansion of data availability in many large-scale, complex, and networked systems, such as surveillance, security, Internet, and finance, it becomes critical to advance the fundamental understanding of knowledge discovery and analysis from raw data to support decision-making processes. Although existing knowledge discovery and data engineering techniques have shown great success in many real-world applications, the problem of learning from imbalanced data (the imbalanced learning problem) is a relatively new challenge that has attracted growing attention from both academia and industry. The imbalanced learning problem is concerned with the performance of learning algorithms in the presence of underrepresented data and severe class distribution skews. Due to the inherent complex characteristics of imbalanced data sets, learning from such data requires new understandings, principles, algorithms, and tools to transform vast amounts of raw data efficiently into information and knowledge representation. In this paper, we provide a comprehensive review of the development of research in learning from imbalanced data. Our focus is to provide a critical review of the nature of the problem, the state-of-the-art technologies, and the current assessment metrics used to evaluate learning performance under the imbalanced learning scenario. Furthermore, in order to stimulate future research in this field, we also highlight the major opportunities and challenges, as well as potential important research directions for learning from imbalanced data.
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paper_title: FRVT 2006 and ICE 2006 Large-Scale Experimental Results

paper_content:

This paper describes the large-scale experimental results from the Face Recognition Vendor Test (FRVT) 2006 and the Iris Challenge Evaluation (ICE) 2006. The FRVT 2006 looked at recognition from high-resolution still frontal face images and 3D face images, and measured performance for still frontal face images taken under controlled and uncontrolled illumination. The ICE 2006 evaluation reported verification performance for both left and right irises. The images in the ICE 2006 intentionally represent a broader range of quality than the ICE 2006 sensor would normally acquire. This includes images that did not pass the quality control software embedded in the sensor. The FRVT 2006 results from controlled still and 3D images document at least an order-of-magnitude improvement in recognition performance over the FRVT 2002. The FRVT 2006 and the ICE 2006 compared recognition performance from high-resolution still frontal face images, 3D face images, and the single-iris images. On the FRVT 2006 and the ICE 2006 data sets, recognition performance was comparable for high-resolution frontal face, 3D face, and the iris images. In an experiment comparing human and algorithms on matching face identity across changes in illumination on frontal face images, the best performing algorithms were more accurate than humans on unfamiliar faces.
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paper_title: FERET (Face Recognition Technology) Recognition Algorithm Development and Test Results

paper_content:

Abstract : As part of the Face Recognition Technology (FERET) program, the U.S. Army Research Laboratory (ARL) conducted supervised government tests and evaluations of automatic face recognition algorithms. The goal of the tests was to provide an independent method of evaluating algorithms and assessing the state of the art in automatic face recognition. This report describes the design and presents the results of the August 1994 and March 1995 FERET tests. Results for FERET tests administered by ARL between August 1994 and August 1996 are reported.
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paper_title: Overview of the face recognition grand challenge

paper_content:

Over the last couple of years, face recognition researchers have been developing new techniques. These developments are being fueled by advances in computer vision techniques, computer design, sensor design, and interest in fielding face recognition systems. Such advances hold the promise of reducing the error rate in face recognition systems by an order of magnitude over Face Recognition Vendor Test (FRVT) 2002 results. The face recognition grand challenge (FRGC) is designed to achieve this performance goal by presenting to researchers a six-experiment challenge problem along with data corpus of 50,000 images. The data consists of 3D scans and high resolution still imagery taken under controlled and uncontrolled conditions. This paper describes the challenge problem, data corpus, and presents baseline performance and preliminary results on natural statistics of facial imagery.
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paper_title: Facial part displacement effect on template-based gender and ethnicity classification

paper_content:

Visual information such as gender, age and ethnicity play critical roles in human identification. Most of gender and ethnicity recognition research works use the full face considering equal discriminant capability for different face parts. In this paper, we improve the gender and ethnicity recognition, by employing the optimum decision making rule on the confidence level of automatically separated face regions using the modified Golden ratio mask. Faces are preprocessed with multiple base point photometric normalization to prevent facial parts displacement in the noted mask, due to different facial parts' distances of people. SVM is employed as the classifier on the extracted Gabor features of each patch to get its confidence level. The final classification results are obtained based on the output of each patch decision using the optimum decision making rule. Finally, using the most accurate normalization approach for each patch, we could achieve 94% and 98% for gender and ethnicity respectively on a dataset composed of FERET and PEAL frontal face images.
---
paper_title: EGA — Ethnicity, gender and age, a pre-annotated face database

paper_content:

Research community achieved considerable progress in face recognition over the past years. Despite this, present face recognition systems are not yet accurate or robust enough to be fully deployed in under-controlled yet high security environments. A number of works have investigated the impact of face categorization on recognition performance, in order to assess the hypothesis that a preliminary face categorization can be used to contain the search space during identification. Categories are usually related to soft-biometrics, such as gender, age, ethnicity. More features can also be used at the same time to define categories (e.g. gender and age). The underlying assumption is that, during identification operations, a sample image is only matched with those pertaining to the same category. Experimental results demonstrate that face categorization based on important visual characteristics such as gender, ethnicity, and age generally improve recognition accuracy, while reducing operation time. On the other hand, it is difficult to appropriately set up related experiments, since available datasets are not organized according to any categorization. Moreover, it is often the case that some features (e.g. ethnicity or gender) are not uniformly represented. For instance, the ethnicity of the research group gathering a dataset, and therefore the location where the enrollment operations are performed, often influences the prevailing ethnical composition of the dataset. As a further example, since most datasets are gathered by enrolling volunteer students, the prevailing age range in most datasets is 20–35. Our contribution relies in an automatic procedure to build a larger multi-racial database, starting from the most popular among the available ones, which automatically reproduces the ethnicity/gender/age categorization that we manually performed in our lab.
---
paper_title: Fusing Local Binary Patterns with Wavelet Features for Ethnicity Identification

paper_content:

Ethnicity identification of face images is of interest in many areas of application, but existing methods are few and limited. This paper presents a fusion scheme that uses block-based uniform local binary patterns and Haar wavelet transform to combine local and global features. In particular, the LL subband coefficients of the whole face are fused with the histograms of uniform local binary patterns from block partitions of the face. We applied the principal component analysis on the fused features and managed to reduce the dimensionality of the feature space from 536 down to around 15 without sacrificing too much accuracy. We have conducted a number of preliminary experiments using a collection of 746 subject face images. The test results show good accuracy and demonstrate the potential of fusing global and local features. The fusion approach is robust, making it easy to further improve the identification at both feature and score levels.
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paper_title: Anthropometric 3D Face Recognition

paper_content:

We present a novel anthropometric three dimensional (Anthroface 3D) face recognition algorithm, which is based on a systematically selected set of discriminatory structural characteristics of the human face derived from the existing scientific literature on facial anthropometry. We propose a novel technique for automatically detecting 10 anthropometric facial fiducial points that are associated with these discriminatory anthropometric features. We isolate and employ unique textural and/or structural characteristics of these fiducial points, along with the established anthropometric facial proportions of the human face for detecting them. Lastly, we develop a completely automatic face recognition algorithm that employs facial 3D Euclidean and geodesic distances between these 10 automatically located anthropometric facial fiducial points and a linear discriminant classifier. On a database of 1149 facial images of 118 subjects, we show that the standard deviation of the Euclidean distance of each automatically detected fiducial point from its manually identified position is less than 2.54 mm. We further show that the proposed Anthroface 3D recognition algorithm performs well (equal error rate of 1.98% and a rank 1 recognition rate of 96.8%), out performs three of the existing benchmark 3D face recognition algorithms, and is robust to the observed fiducial point localization errors.
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paper_title: Recognizing moving faces: A psychological and neural synthesis

paper_content:

Information for identifying a human face can be found both in the invariant structure of features and in idiosyncratic movements and gestures. When both kinds of information are available, psychological evidence indicates that: (1) dynamic information contributes more to recognition under non-optimal viewing conditions, e.g. poor illumination, low image resolution, recognition from a distance; (2) dynamic information contributes more as a viewer's experience with the face increases; and (3) a structure-from-motion analysis can make a perceptually based contribution to face recognition. A recently proposed distributed neural system for face perception, with minor modifications, can accommodate the psychological findings with moving faces.
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paper_title: On a taxonomy of facial features

paper_content:

After nearly a decade of intensive research in face recognition, no standard organization exists for grouping the salient information available in 2D face images into feature categories. At the same time, human verification of a subject's identity based on facial images lacks a consistent methodology. In this paper we propose a taxonomy of available facial features that: (i) serves as a precursor to studies on the individuality of facial features, (ii) follows a similar well established and accepted organization for fingerprint features, and (iii) contains features computable by both machines and humans as well as by machines alone. This manuscript is intended as a strawman of an organization of facial features, that would hopefully lead to a standardization of such features. Such a facial feature organization will (i) enable studies on the individuality of facial features, which has important ramifications for the acceptance of expert testimony in legal proceedings for determining the identity of an individual from a facial photograph, and (ii) help standardize the framework of commercial face recognition systems.
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paper_title: A survey of approaches and challenges in 3D and multi-modal 3D + 2D face recognition

paper_content:

This survey focuses on recognition performed by matching models of the three-dimensional shape of the face, either alone or in combination with matching corresponding two-dimensional intensity images. Research trends to date are summarized, and challenges confronting the development of more accurate three-dimensional face recognition are identified. These challenges include the need for better sensors, improved recognition algorithms, and more rigorous experimental methodology.
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paper_title: Texas 3D Face Recognition Database

paper_content:

We make the Texas 3D Face Recognition Database available to researchers in three dimensional (3D) face recognition and other related areas. This database contains 1149 pairs of high resolution, pose normalized, preprocessed, and perfectly aligned color and range images of 118 adult human subjects acquired using a stereo camera. The images are accompanied with information about the subjects' gender, ethnicity, facial expression, and the locations of 25 manually located anthropometric facial fiducial points. Specific partitions of the data for developing and evaluating 3D face recognition algorithms are also included.
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paper_title: Hajj and Umrah Event Recognition Datasets

paper_content:

In this note, new Hajj and Umrah Event Recognition datasets (HUER) are presented. The demonstrated datasets are based on videos and images taken during 2011-2012 Hajj and Umrah seasons. HUER is the first collection of datasets covering the six types of Hajj and Umrah ritual events (rotating in Tawaf around Kabaa, performing Sa'y between Safa and Marwa, standing on the mount of Arafat, staying overnight in Muzdalifah, staying two or three days in Mina, and throwing Jamarat). The HUER datasets also contain video and image databases for nine types of human actions during Hajj and Umrah (walking, drinking from Zamzam water, sleeping, smiling, eating, praying, sitting, shaving hairs and ablutions, reading the holy Quran and making duaa). The spatial resolutions are 1280 x 720 pixels for images and 640 x 480 pixels for videos and have lengths of 20 seconds in average with 30 frame per second rates.
---
paper_title: Facial part displacement effect on template-based gender and ethnicity classification

paper_content:

Visual information such as gender, age and ethnicity play critical roles in human identification. Most of gender and ethnicity recognition research works use the full face considering equal discriminant capability for different face parts. In this paper, we improve the gender and ethnicity recognition, by employing the optimum decision making rule on the confidence level of automatically separated face regions using the modified Golden ratio mask. Faces are preprocessed with multiple base point photometric normalization to prevent facial parts displacement in the noted mask, due to different facial parts' distances of people. SVM is employed as the classifier on the extracted Gabor features of each patch to get its confidence level. The final classification results are obtained based on the output of each patch decision using the optimum decision making rule. Finally, using the most accurate normalization approach for each patch, we could achieve 94% and 98% for gender and ethnicity respectively on a dataset composed of FERET and PEAL frontal face images.
---
paper_title: EGA — Ethnicity, gender and age, a pre-annotated face database

paper_content:

Research community achieved considerable progress in face recognition over the past years. Despite this, present face recognition systems are not yet accurate or robust enough to be fully deployed in under-controlled yet high security environments. A number of works have investigated the impact of face categorization on recognition performance, in order to assess the hypothesis that a preliminary face categorization can be used to contain the search space during identification. Categories are usually related to soft-biometrics, such as gender, age, ethnicity. More features can also be used at the same time to define categories (e.g. gender and age). The underlying assumption is that, during identification operations, a sample image is only matched with those pertaining to the same category. Experimental results demonstrate that face categorization based on important visual characteristics such as gender, ethnicity, and age generally improve recognition accuracy, while reducing operation time. On the other hand, it is difficult to appropriately set up related experiments, since available datasets are not organized according to any categorization. Moreover, it is often the case that some features (e.g. ethnicity or gender) are not uniformly represented. For instance, the ethnicity of the research group gathering a dataset, and therefore the location where the enrollment operations are performed, often influences the prevailing ethnical composition of the dataset. As a further example, since most datasets are gathered by enrolling volunteer students, the prevailing age range in most datasets is 20–35. Our contribution relies in an automatic procedure to build a larger multi-racial database, starting from the most popular among the available ones, which automatically reproduces the ethnicity/gender/age categorization that we manually performed in our lab.
---
paper_title: Fusing Local Binary Patterns with Wavelet Features for Ethnicity Identification

paper_content:

Ethnicity identification of face images is of interest in many areas of application, but existing methods are few and limited. This paper presents a fusion scheme that uses block-based uniform local binary patterns and Haar wavelet transform to combine local and global features. In particular, the LL subband coefficients of the whole face are fused with the histograms of uniform local binary patterns from block partitions of the face. We applied the principal component analysis on the fused features and managed to reduce the dimensionality of the feature space from 536 down to around 15 without sacrificing too much accuracy. We have conducted a number of preliminary experiments using a collection of 746 subject face images. The test results show good accuracy and demonstrate the potential of fusing global and local features. The fusion approach is robust, making it easy to further improve the identification at both feature and score levels.
---
paper_title: A high-resolution 3D dynamic facial expression database

paper_content:

Face information processing relies on the quality of data resource. From the data modality point of view, a face database can be 2D or 3D, and static or dynamic. From the task point of view, the data can be used for research of computer based automatic face recognition, face expression recognition, face detection, or cognitive and psychological investigation. With the advancement of 3D imaging technologies, 3D dynamic facial sequences (called 4D data) have been used for face information analysis. In this paper, we focus on the modality of 3D dynamic data for the task of facial expression recognition. We present a newly created high-resolution 3D dynamic facial expression database, which is made available to the scientific research community. The database contains 606 3D facial expression sequences captured from 101 subjects of various ethnic backgrounds. The database has been validated through our facial expression recognition experiment using an HMM based 3D spatio-temporal facial descriptor. It is expected that such a database shall be used to facilitate the facial expression analysis from a static 3D space to a dynamic 3D space, with a goal of scrutinizing facial behavior at a higher level of detail in a real 3D spatio-temporal domain.
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paper_title: Iranian Face Database with age, pose and expression

paper_content:

This paper introduces a database of over 3,600 color images which we collected from 616 different human faces. It includes facial images of people between ages 2-85. Face images of persons with different ages is needed to generate a reliable age classification algorithm. Four different poses, two different expressions, and one image with glasses were taken if the subjects were interested. Information such as career, kind of skin, fingerprint, and cosmetic points (surgical points, fracture or laceration on face) is taken from participants. The background was a white frame and the only illumination was the daylight. We named this database the Iranian face database (IFDB). To evaluate the database the experimental result of a new feature detection algorithm is reported.
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paper_title: The humanID gait challenge problem: data sets, performance, and analysis

paper_content:

Identification of people by analysis of gait patterns extracted from video has recently become a popular research problem. However, the conditions under which the problem is "solvable" are not understood or characterized. To provide a means for measuring progress and characterizing the properties of gait recognition, we introduce the humanlD gait challenge problem. The challenge problem consists of a baseline algorithm, a set of 12 experiments, and a large data set. The baseline algorithm estimates silhouettes by background subtraction and performs recognition by temporal correlation of silhouettes. The 12 experiments are of increasing difficulty, as measured by the baseline algorithm, and examine the effects of five covariates on performance. The covariates are: change in viewing angle, change in shoe type, change in walking surface, carrying or not carrying a briefcase, and elapsed time between sequences being compared. Identification rates for the 12 experiments range from 78 percent on the easiest experiment to 3 percent on the hardest. All five covariates had statistically significant effects on performance, with walking surface and time difference having the greatest impact. The data set consists of 1,870 sequences from 122 subjects spanning five covariates (1.2 gigabytes of data). This infrastructure supports further development of gait recognition algorithms and additional experiments to understand the strengths and weaknesses of new algorithms. The experimental results are presented, the more detailed is the possible meta-analysis and greater is the understanding. It is this potential from the adoption of this challenge problem that represents a radical departure from traditional computer vision research methodology.
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paper_title: Shape analysis of 3D head scan data for U.S. respirator users

paper_content:

In 2003, the National Institute for Occupational Safety and Health (NIOSH) conducted a head-and-face anthropometric survey of diverse, civilian respirator users. Of the 3,997 subjects measured using traditional anthropometric techniques, surface scans and 26 three-dimensional (3D) landmark locations were collected for 947 subjects. The objective of this study was to report the size and shape variation of the survey participants using the 3D data. Generalized Procrustes Analysis (GPA) was conducted to standardize configurations of landmarks associated with individuals into a common coordinate system. The superimposed coordinates for each individual were used as commensurate variables that describe individual shape and were analyzed using Principal Component Analysis (PCA) to identify population variation. The first four principal components (PC) account for 49% of the total sample variation. The first PC indicates that overall size is an important component of facial variability. The second PC accounts for long and narrow or short and wide faces. Longer narrow orbits versus shorter wider orbits can be described by PC3, and PC4 represents variation in the degree of ortho/prognathism. Geometric Morphometrics provides a detailed and interpretable assessment of morphological variation that may be useful in assessing respirators and devising new test and certification standards.
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paper_title: The CAESAR project: a 3-D surface anthropometry survey

paper_content:

The CAESAR study (Civilian American and European Surface Anthropometry Resource) is a survey of body measurements for people ages 18-65 in three countries: USA, The Netherlands, and Italy. It is a collaborative effort with partners from several countries and many industries. It employs the latest in 3D measurement technology, and is the first 3D surface anthropometry survey of the US and Europe. Data from the study will allow us to effectively design everything from better fitting clothes and protective equipment to better seats and workstations, as well as dramatically reducing the cost to design and manufacture them.
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paper_title: THE CHINESE FACE: A 3D ANTHROPOMETRIC ANALYSIS

paper_content:

Traditional facial anthropometry has a long history yet few studies have been conducted using the latest 3D scanning technology. The majority of these facial studies have focused on Caucasian populations. The recent SizeChina survey has collected high resolution 3D data from over 2000 adult Chinese subjects. The 3D facial data of 772 females from the SizeChina database were analyzed in this study. This data was pre-processed to eliminate scan noise, fill holes caused by shadow effect and aligned to a common plane. Thirty one facial landmarks were used for statistical analysis and to create a 3D anthropometric face model. Simple statistics results demonstrated that Chinese females in general have wider and shorter faces compared to other cultures. Correlation analysis showed the breadth dimensions are highly correlated and it seems that heavier people have larger bigonial breadth. Results from the principal component analysis on anthropometric face model reveals that major variations of face shapes are related to face breadth, face depth, face length, chin and jaw position. The results of this study have significant implications for the design of facial products for Chinese women.
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paper_title: A comparison between Chinese and Caucasian head shapes.

paper_content:

Univariate anthropometric data have long documented a difference in head shape proportion between Chinese and Caucasian populations. This difference has made it impossible to create eyewear, helmets and facemasks that fit both groups well. However, it has been unknown to what extend and precisely how the two populations differ from each other in form. In this study, we applied geometric morphometrics to dense surface data to quantify and characterize the shape differences using a large data set from two recent 3D anthropometric surveys, one in North America and Europe, and one in China. The comparison showed the significant variations between head shapes of the two groups and results demonstrated that Chinese heads were rounder than Caucasian counterparts, with a flatter back and forehead. The quantitative measurements and analyses of these shape differences may be applied in many fields, including anthropometrics, product design, cranial surgery and cranial therapy.
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paper_title: Multi-scale capture of facial geometry and motion

paper_content:

We present a novel multi-scale representation and acquisition method for the animation of high-resolution facial geometry and wrinkles. We first acquire a static scan of the face including reflectance data at the highest possible quality. We then augment a traditional marker-based facial motion-capture system by two synchronized video cameras to track expression wrinkles. The resulting model consists of high-resolution geometry, motion-capture data, and expression wrinkles in 2D parametric form. This combination represents the facial shape and its salient features at multiple scales. During motion synthesis the motion-capture data deforms the high-resolution geometry using a linear shell-based mesh-deformation method. The wrinkle geometry is added to the facial base mesh using nonlinear energy optimization. We present the results of our approach for performance replay as well as for wrinkle editing.
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paper_title: A Spiking Neural Network Based Cortex-Like Mechanism and Application to Facial Expression Recognition

paper_content:

In this paper, we present a quantitative, highly structured cortex-simulated model, which can be simply described as feedforward, hierarchical simulation of ventral stream of visual cortex using biologically plausible, computationally convenient spiking neural network system. The motivation comes directly from recent pioneering works on detailed functional decomposition analysis of the feedforward pathway of the ventral stream of visual cortex and developments on artificial spiking neural networks (SNNs). By combining the logical structure of the cortical hierarchy and computing power of the spiking neuron model, a practical framework has been presented. As a proof of principle, we demonstrate our system on several facial expression recognition tasks. The proposed cortical-like feedforward hierarchy framework has the merit of capability of dealing with complicated pattern recognition problems, suggesting that, by combining the cognitive models with modern neurocomputational approaches, the neurosystematic approach to the study of cortex-like mechanism has the potential to extend our knowledge of brain mechanisms underlying the cognitive analysis and to advance theoretical models of how we recognize face or, more specifically, perceive other people's facial expression in a rich, dynamic, and complex environment, providing a new starting point for improved models of visual cortex-like mechanism.
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paper_title: FACSGen: A Tool to Synthesize Emotional Facial Expressions Through Systematic Manipulation of Facial Action Units

paper_content:

To investigate the perception of emotional facial expressions, researchers rely on shared sets of photos or videos, most often generated by actor portrayals. The drawback of such standardized material is a lack of flexibility and controllability, as it does not allow the systematic parametric manipulation of specific features of facial expressions on the one hand, and of more general properties of the facial identity (age, ethnicity, gender) on the other. To remedy this problem, we developed FACSGen: a novel tool that allows the creation of realistic synthetic 3D facial stimuli, both static and dynamic, based on the Facial Action Coding System. FACSGen provides researchers with total control over facial action units, and corresponding informational cues in 3D synthetic faces. We present four studies validating both the software and the general methodology of systematically generating controlled facial expression patterns for stimulus presentation.
---
paper_title: Spiking neural networks based cortex like mechanism: A case study for facial expression recognition

paper_content:

Ongoing efforts within neuroscience and intelligent system have been directed toward the building of artificial computational models using simulated neuron units as basic building blocks. Such efforts, inspired in the standard design of traditional neural networks, are limited by the difficulties arising from single functional performance and computational inconvenience, especially when modeling large scale, complex and dynamic processes such as cognitive recognition. Here, we show that there is a different form of implementing cortex-like mechanism, the motivation comes directly from recent pioneering works on detailed functional decomposition analysis of the visual cortex and developments on spiking neural networks (SNNs), a promising direction for neural networks, as they utilize information representation as trains of spikes, embedded with spatiotemporal characteristics. A practical implementation is presented, which can be simply described as cortical-like feed-forward hierarchy using biologically plausible neural system. As a proof of principle, a prototype model has been testified on the platform of several facial expression dataset. Of note, small structure modifications and different learning schemes allow for implementing more complicated decision system, showing great potential for discovering implicit pattern of interest and further analysis. Our results support the approach of using such hierarchical consortia as an efficient way of complex pattern analysis task not easily solvable using traditional, single functional way of implementations.
---
paper_title: Applying the attractor field model to social cognition: Perceptual discrimination is facilitated, but memory is impaired for faces displaying evaluatively congruent expressions.

paper_content:

A new model of mental representation is applied to social cognition: the attractor field model. Using the model, the authors predicted and found a perceptual advantage but a memory disadvantage for faces displaying evaluatively congruent expressions. In Experiment 1, participants completed a same/different perceptual discrimination task involving morphed pairs of angry-to-happy Black and White faces. Pairs of faces displaying evaluatively incongruent expressions (i.e., happy Black, angry White) were more likely to be labeled as similar and were less likely to be accurately discriminated from one another than faces displaying evaluatively congruent expressions (i.e., angry Black, happy White). Experiment 2 replicated this finding and showed that objective discriminability of stimuli moderated the impact of attractor field effects on perceptual discrimination accuracy. In Experiment 3, participants completed a recognition task for angry and happy Black and White faces. Consistent with the attractor field model, memory accuracy was better for faces displaying evaluatively incongruent expressions. Theoretical and practical implications of these findings are discussed.
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paper_title: Virtual human technology: Capturing sex, race, and age influences in individual pain decision policies

paper_content:

Pain assessment is subject to bias due to characteristics of the individual in pain and of the observing person. Few research studies have examined pain assessment biases in an experimental setting. The present study employs innovative virtual human technology to achieve greater experimental control. A lens model design was used to capture decision-making policies at the idiographic and nomothetic level. Seventy-five undergraduates viewed virtual humans (VH) that varied in sex, race, age, and pain expression. Participants provided computerized ratings with Visual Analogue Scales on the VH's pain intensity, pain unpleasantness, negative mood, coping, and need for medical treatment. Idiographic analyses revealed that individuals used pain expression most frequently as a significant cue. Nomothetic analyses showed that higher pain expression VH and female VH were viewed as having higher pain intensity, higher pain unpleasantness, greater negative mood, worse coping, and a greater need to seek medical treatment than lower pain expression VH and male VH, respectively. Older VH were viewed as having worse coping and a greater need to seek medical treatment than younger VH. This innovative paradigm involving VH technology and a lens model design was shown to be highly effective and could serve as a model for future studies investigating pain-related decision making in healthcare providers.
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paper_title: Face Recognition Performance: Role of Demographic Information

paper_content:

This paper studies the influence of demographics on the performance of face recognition algorithms. The recognition accuracies of six different face recognition algorithms (three commercial, two nontrainable, and one trainable) are computed on a large scale gallery that is partitioned so that each partition consists entirely of specific demographic cohorts. Eight total cohorts are isolated based on gender (male and female), race/ethnicity (Black, White, and Hispanic), and age group (18-30, 30-50, and 50-70 years old). Experimental results demonstrate that both commercial and the nontrainable algorithms consistently have lower matching accuracies on the same cohorts (females, Blacks, and age group 18-30) than the remaining cohorts within their demographic. Additional experiments investigate the impact of the demographic distribution in the training set on the performance of a trainable face recognition algorithm. We show that the matching accuracy for race/ethnicity and age cohorts can be improved by training exclusively on that specific cohort. Operationally, this leads to a scenario, called dynamic face matcher selection, where multiple face recognition algorithms (each trained on different demographic cohorts) are available for a biometric system operator to select based on the demographic information extracted from a probe image. This procedure should lead to improved face recognition accuracy in many intelligence and law enforcement face recognition scenarios. Finally, we show that an alternative to dynamic face matcher selection is to train face recognition algorithms on datasets that are evenly distributed across demographics, as this approach offers consistently high accuracy across all cohorts.
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paper_title: Can soft biometric traits assist user recognition?

paper_content:

Biometrics is rapidly gaining acceptance as the technology that can meet the ever increasing need for security in critical applications. Biometric systems automatically recognize individuals based on their physiological and behavioral characteristics. Hence, the fundamental requirement of any biometric recognition system is a human trait having several desirable properties like universality, distinctiveness, permanence, collectability, acceptability, and resistance to circumvention. However, a human characteristic that possesses all these properties has not yet been identified. As a result, none of the existing biometric systems provide perfect recognition and there is a scope for improving the performance of these systems. Although characteristics like gender, ethnicity, age, height, weight and eye color are not unique and reliable, they provide some information about the user. We refer to these characteristics as "soft" biometric traits and argue that these traits can complement the identity information provided by the primary biometric identifiers like fingerprint and face. This paper presents the motivation for utilizing soft biometric information and analyzes how the soft biometric traits can be automatically extracted and incorporated in the decision making process of the primary biometric system. Preliminary experiments were conducted on a fingerprint database of 160 users by synthetically generating soft biometric traits like gender, ethnicity, and height based on known statistics. The results show that the use of additional soft biometric user information significantly improves (approximately 6%) the recognition performance of the fingerprint biometric system.
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paper_title: Human races: Classifying people vs. understanding diversity

paper_content:

The idea that all humans naturally belong to one of a few biological types or races that evolved in isolation was unchallenged for centuries, but large-scale modern studies failed to associate racial labels with recognizable genetic clus- ters. Recently, the conclusions of those studies have been questioned by authors who argue that racial classification has objective scientific bases and is indispensable in epidemiology and genetics. However, no classificatio n is useful if the classification units are vague or controversial , and no consensus was ever reached on the number and definition of the human races. The available studies show that there is geographic structure in human genome diversity, and that it is possi- ble to infer with reasonable accuracy the continent of origin from an individual's multilocus genotype. However, clear-cut genetic boundaries between human groups, which would be necessary to recognise these groups as relatively isolated mating units which zoologists would call races, have not been identified so far. On the contrary, allele frequencies and synthetic descriptors of genetic variation appear distributed in gradients over much of the planet, which points to gene flow, rather than to isolation, as the main evolutionary force shaping human genome diversity. A better understanding of patterns of human diversity and of the underlying evolutionary processes is important for its own sake, but is also indis- pensable for the development of diagnostic and therapeutic tools designed for the individual genotype, rather than for ill- defined race-specific genotypes.
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paper_title: Instant Human Face Attributes Recognition System

paper_content:

The objective of this work is to provide a simple and yet efficient tool for human attributes like gender, age and ethnicity by the human facial image in the real time image as we all aware this term that “Real-Time frame rate is a vital factor for practical deployment of computer vision system”. In this particular paper we are trying to presents the progress towards face detection and human attributes classification system. We have developed an algorithm for the classification of gender, age and race from human frontal facial image As the basis of the classifier proposed algorithm uses training set neuron receptors that process visual information a study of the several variants of these classifiers and shows the principal possibility of sex determination, assessment of a person's age on a scale (adult - children) and recognition of race by using the neuron-like receptors.
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paper_title: Manipulation of non-verbal interaction style and demographic embodiment to increase anthropomorphic computer character credibility

paper_content:

For years, people have sought more natural means of communicating with their computers. Many have suggested that interaction with a computer should be as easy as interacting with other people, taking advantage of the multimodal nature of human communication. While users should, in theory, gravitate to such anthropomorphic embodiments, quite the contrary has been experienced; users generally have been dissatisfied and abandoned their use. This suggests a disconnect between factors that make human-human communication engaging and those used by designers to support human-agent interaction. This paper discusses a set of empirical studies that attempted to replicate human-human non-verbal behavior. The focus revolved around behaviors that portray a credible facade, thereby helping embodied conversational agents (ECAs) to form a successful cooperative dyad with users. Based on a review of the non-verbal literature, a framework was created that identified trustworthy and credible non-verbal behaviors across five areas and formed design guidelines for character interaction. The design suggestions for those areas emanating from the facial region were experimentally supported but there was no concordant increase in perceived trust when bodily regions (posture, gesture) were added. In addition, in examining the importance of demographic elements in embodiment, it was found that users prefer to interact with characters that match their ethnicity and are young looking. There was no significant preference for gender. The implications of these results, as well as other interesting consequences are discussed.
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paper_title: Cross-Racial/Ethnic Eyewitness Identification: A Field Study1

paper_content:

Using a method similar to that of Brigham, Maass, Snyder, and Spaudling (1982), 86 adult convenience store clerks were asked to identify 3 confederate/customers, one Anglo-American, one black-American, and one Mexican-American who had visited their stores 2 hours earlier. Wilcoxon matched pairs signed-rank test showed that clerks were superior at identifying customers of their own racial or ethnic group, Z= 2.84, p < .002. For clerks who made an identification, the correlation between the recognition accuracy and confidence in their identification was not significant, rpbis (56)= .05. The results of this field study paralleled those found in most laboratory experiments of cross-racial/ethnic identification and demonstrated that Mexican-Americans are subject to the own/other groups identification accuracy phenomenon as well.
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paper_title: Interactive evolutionary generation of facial composites for locating suspects in criminal investigations

paper_content:

Statistical appearance models have previously been used for computer face recognition applications in which an image patch is synthesized and morphed to match a target face image using an automated iterative fitting algorithm. Here we describe an alternative use for appearance models, namely for producing facial composite images (sometimes referred to as E-FIT or PhotoFIT images). This application poses an interesting real-world optimization problem because the target face exists in the mind of the witness and not in a tangible form such as a digital image. To solve this problem we employ an interactive evolutionary algorithm that allows the witness to evolve a likeness to the target face. A system based on our approach, called EFIT-V, is used frequently by three quarters of UK police constabularies.
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paper_title: On a taxonomy of facial features

paper_content:

After nearly a decade of intensive research in face recognition, no standard organization exists for grouping the salient information available in 2D face images into feature categories. At the same time, human verification of a subject's identity based on facial images lacks a consistent methodology. In this paper we propose a taxonomy of available facial features that: (i) serves as a precursor to studies on the individuality of facial features, (ii) follows a similar well established and accepted organization for fingerprint features, and (iii) contains features computable by both machines and humans as well as by machines alone. This manuscript is intended as a strawman of an organization of facial features, that would hopefully lead to a standardization of such features. Such a facial feature organization will (i) enable studies on the individuality of facial features, which has important ramifications for the acceptance of expert testimony in legal proceedings for determining the identity of an individual from a facial photograph, and (ii) help standardize the framework of commercial face recognition systems.
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paper_title: Describable Visual Attributes for Face Verification and Image Search

paper_content:

We introduce the use of describable visual attributes for face verification and image search. Describable visual attributes are labels that can be given to an image to describe its appearance. This paper focuses on images of faces and the attributes used to describe them, although the concepts also apply to other domains. Examples of face attributes include gender, age, jaw shape, nose size, etc. The advantages of an attribute-based representation for vision tasks are manifold: They can be composed to create descriptions at various levels of specificity; they are generalizable, as they can be learned once and then applied to recognize new objects or categories without any further training; and they are efficient, possibly requiring exponentially fewer attributes (and training data) than explicitly naming each category. We show how one can create and label large data sets of real-world images to train classifiers which measure the presence, absence, or degree to which an attribute is expressed in images. These classifiers can then automatically label new images. We demonstrate the current effectiveness-and explore the future potential-of using attributes for face verification and image search via human and computational experiments. Finally, we introduce two new face data sets, named FaceTracer and PubFig, with labeled attributes and identities, respectively.
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paper_title: Automated person categorization for video surveillance using soft biometrics

paper_content:

We present a prototype video tracking and person categorization system that uses face and person soft biometric features ::: to tag people while tracking them in multiple camera views. Our approach takes advantage of temporal aspect of video ::: by extracting and accumulating feasible soft biometric features for each person in every frame to build a dynamic soft ::: biometric feature list for each tracked person in surveillance videos. We developed algorithms for extracting face soft ::: biometric features to achieve gender and ethnicity classification and session soft biometric features to aid in camera ::: hand-off in surveillance videos with low resolution and uncontrolled illumination. To train and test our face soft ::: biometry algorithms, we collected over 1500 face images from both genders and three ethnicity groups with various ::: sizes, poses and illumination. These soft biometric feature extractors and classifiers are implemented on our existing ::: video content extraction platform to enhance video surveillance tasks. Our algorithms achieved promising results for ::: gender and ethnicity classification, and tracked person re-identification for camera hand-off on low to good quality ::: surveillance and broadcast videos. By utilizing the proposed system, a high level description of extracted person's soft ::: biometric data can be stored to use later for different purposes, such as to provide categorical information of people, to ::: create database partitions to accelerate searches in responding to user queries, and to track people between cameras.
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paper_title: Fuzzy Moments Method for Face Recognition in an Ethnic Database

paper_content:

So far, numerous methods have been proposed for face recognition based on single image person. In this paper, we introduce a new recognition algorithm based on feature vectors of three types of moments and fuzzy combination of them. We used an ethnic database in our experiments that are distinctly different from other famous databases such as AR and FERET. In ethnic database women have Hijab that is important feature in Muslim community such as Arabian countries. Also most of men have beard or mustache in these nations. Experimental results show that these features increase the recognition rate. Really, these characteristics eliminate the false recognition between male and female in the process. Subsets of 100 images from ethnic, AR and FERET are used in our experiments. Proposed method achieves 96% accuracy for ethnic database that is higher than two other databases.
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paper_title: Looking at human-computer interface design: Effects of ethnicity in computer agents

paper_content:

This paper presents empirical research findings that identify demonstrated attitude changes in computer users associated with their receiving advice from personified computer agents of two different ethnicities: African American and European American. Our findings indicate that computer users are more likely to change their actions (demonstrating underlying attitudes) based on input from a computer agent whose ethnicity is similar to theirs. These findings directly impact computer agent design in many fields.
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paper_title: Ethnicity- and Gender-based Subject Retrieval Using 3-D Face-Recognition Techniques

paper_content:

While the retrieval of datasets from human subjects based on demographic characteristics such as gender or race is an ability with wide-ranging application, it remains poorly-studied. In contrast, a large body of work exists in the field of biometrics which has a different goal: the recognition of human subjects. Due to this disparity of interest, existing methods for retrieval based on demographic attributes tend to lag behind the more well-studied algorithms designed purely for face matching. The question this raises is whether a face recognition system could be leveraged to solve these other problems and, if so, how effective it could be. In the current work, we explore the limits of such a system for gender and ethnicity identification given (1) a ground truth of demographically-labeled, textureless 3-D models of human faces and (2) a state-of-the-art face-recognition algorithm. Once trained, our system is capable of classifying the gender and ethnicity of any such model of interest. Experiments are conducted on 4007 facial meshes from the benchmark Face Recognition Grand Challenge v2 dataset.
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paper_title: THE CHINESE FACE: A 3D ANTHROPOMETRIC ANALYSIS

paper_content:

Traditional facial anthropometry has a long history yet few studies have been conducted using the latest 3D scanning technology. The majority of these facial studies have focused on Caucasian populations. The recent SizeChina survey has collected high resolution 3D data from over 2000 adult Chinese subjects. The 3D facial data of 772 females from the SizeChina database were analyzed in this study. This data was pre-processed to eliminate scan noise, fill holes caused by shadow effect and aligned to a common plane. Thirty one facial landmarks were used for statistical analysis and to create a 3D anthropometric face model. Simple statistics results demonstrated that Chinese females in general have wider and shorter faces compared to other cultures. Correlation analysis showed the breadth dimensions are highly correlated and it seems that heavier people have larger bigonial breadth. Results from the principal component analysis on anthropometric face model reveals that major variations of face shapes are related to face breadth, face depth, face length, chin and jaw position. The results of this study have significant implications for the design of facial products for Chinese women.
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paper_title: A comparison between Chinese and Caucasian head shapes.

paper_content:

Univariate anthropometric data have long documented a difference in head shape proportion between Chinese and Caucasian populations. This difference has made it impossible to create eyewear, helmets and facemasks that fit both groups well. However, it has been unknown to what extend and precisely how the two populations differ from each other in form. In this study, we applied geometric morphometrics to dense surface data to quantify and characterize the shape differences using a large data set from two recent 3D anthropometric surveys, one in North America and Europe, and one in China. The comparison showed the significant variations between head shapes of the two groups and results demonstrated that Chinese heads were rounder than Caucasian counterparts, with a flatter back and forehead. The quantitative measurements and analyses of these shape differences may be applied in many fields, including anthropometrics, product design, cranial surgery and cranial therapy.
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paper_title: Silhouetted face profiles: A new methodology for face perception research

paper_content:

We present a new methodology for constructing face stimuli for psychological experiments based on silhouetted face profiles. Face silhouettes carry a number of theoretical and methodological advantages compared to more complex face stimuli and lend themselves to a simple yet powerful parameterization. In five behavioral studies, we show that face silhouettes are processed like regular face stimuli: They provide enough information for accurate gender judgments (Study 1), age estimations (Study 2), and reliable and cross-valid attractiveness ratings (Study 3). Furthermore, face silhouettes elicit an inversion effect (Study 4) and allow for remarkably accurate cross-identification with front-view photographs (Study 5). We then describe a shape-based parameterization that relies on a small set of landmark points and show that face silhouettes can be effectively represented in a 20-dimensional ‘‘silhouette face space’’ (Study 6). We show that in this physical space, distance from the center of the space corresponds to perceived distinctiveness (Study 7), confirming a key axiom in the formulation of the face space model. Finally, we discuss straightforward applications of the face silhouette methodology and address some limitations.
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paper_title: Gender and ethnicity identification from silhouetted face profiles

paper_content:

This paper demonstrates, to our best knowledge, the first attempt on gender and ethnicity identification from silhouetted face profiles using a computer vision technique. The results achieved, after testing on 441 images, show that silhouetted face profiles have a lot of information, in particular, for ethnicity identification. Shape context based matching [1] was employed for classification. The test samples were multi-ethnic. Average accuracy for gender was 71.20% and for ethnicity 71.66%. However, the accuracy was significantly higher for some classes, such as 83.41% for females (in case of gender identification) and 80.37% for East and South East Asians (in case of ethnicity identification).
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paper_title: Gender and Race Identification by Man and Machine

paper_content:

This work details a comprehensive study on gender and race identification from different facial representations. The major contributions of this work are: comparison of human and machine performance, qualitative analysis of use of color for race identification, combining different facial views for gender identification and extensive human experiments for both gender and race recognition from four different facial representations.
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paper_title: Fusing with context: A Bayesian approach to combining descriptive attributes

paper_content:

For identity related problems, descriptive attributes can take the form of any information that helps represent an individual, including age data, describable visual attributes, and contextual data. With a rich set of descriptive attributes, it is possible to enhance the base matching accuracy of a traditional face identification system through intelligent score weighting. If we can factor any attribute differences between people into our match score calculation, we can deemphasize incorrect results, and ideally lift the correct matching record to a higher rank position. Naturally, the presence of all descriptive attributes during a match instance cannot be expected, especially when considering non-biometric context. Thus, in this paper, we examine the application of Bayesian Attribute Networks to combine descriptive attributes and produce accurate weighting factors to apply to match scores from face recognition systems based on incomplete observations made at match time. We also examine the pragmatic concerns of attribute network creation, and introduce a Noisy-OR formulation for streamlined truth value assignment and more accurate weighting. Experimental results show that incorporating descriptive attributes into the matching process significantly enhances face identification over the baseline by up to 32.8%.
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paper_title: Demographic classification from face videos using manifold learning

paper_content:

Research on automatic demographic classification is still in its infancy despite the vast potential applications. The few existing works are only based on static images while nowadays input data in many real-world applications consist of video sequences. From these observations and also inspired by studies in neuroscience emphasizing manifold ways of visual perception, we propose in this work a novel approach to demographic classification from video sequences which encodes and exploits the correlation between the face images through manifold learning. Our extensive experiments on the gender and age classification problems show that the proposed manifold learning based approach yields in excellent results outperforming those of traditional static image based methods. Furthermore, to gain insight into the proposed approach, we also investigate an LBP (local binary patterns) based spatiotemporal method as a baseline system for combining spatial and temporal information to demographic classification from videos.
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paper_title: The Manifold Ways of Perception

paper_content:

One of the great puzzles of visual perception is how an image that is in perpetual flux can still be seen by the observer as the same object. In an informative Perspective, ::: Seung and Lee ::: explain the mathematical intricacies of two new algorithms for modeling the variability of perceptual stimuli and other types of high-dimensional data ( ::: Tenenbaum ::: et al., and ::: Roweis and Saul).
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paper_title: Textured 3D face recognition using biological vision-based facial representation and optimized weighted sum fusion

paper_content:

This paper proposes a novel biological vision-based facial description, namely Perceived Facial Images (PFIs), aiming to highlight intra-class and inter-class variations of both facial range and texture images for textured 3D face recognition. These generated PFIs simulate the response of complex neurons to gradient information within a certain neighborhood and possess the properties of being highly distinctive and robust to affine illumination and geometric transformation. Based on such an intermediate facial representation, SIFT-based matching is further carried out to calculate similarity scores between a given probe face and the gallery ones. Because the facial description generates a PFI for each quantized gradient orientation of range and texture faces, we then propose a score level fusion strategy which optimizes the weights using a genetic algorithm in a learning step. Evaluated on the entire FRGC v2.0 database, the rank-one recognition rate using only 3D or 2D modality is 95.5% and 95.9%, respectively; while fusing both modalities, i.e. range and texture-based PFIs, the final accuracy is 98.0%, demonstrating the effectiveness of the proposed biological vision-based facial description and the optimized weighted sum fusion.
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paper_title: Registration of 3D Face Scans with Average Face Models

paper_content:

htmlabstractThe accuracy of a 3D face recognition system depends on a correct registration that aligns the facial surfaces and makes a comparison possible. The best results obtained so far use a costly one-to-all registration approach, which requires the registration of each facial surface to all faces in the gallery. We explore the approach of registering the new facial surface to an average face model (AFM), which automatically establishes correspondence to the pre-registered gallery faces. We propose a new algorithm for constructing an AFM, and show that it works better than a recent approach. Extending the single-AFM approach, we propose to employ category-specific alternative AFMs for registration, and evaluate the effect on subsequent classification. We perform simulations with multiple AFMs that correspond to different clusters in the face shape space and compare these with gender and morphology based groupings. We show that the automatic clustering approach separates the faces into gender and morphology groups, consistent with the other race effect reported in the psychology literature. We inspect thin-plate spline and iterative closest point based registration schemes under manual or automatic landmark detection prior to registration. Finally, we describe and analyse a regular re-sampling method that significantly increases the accuracy of registration.
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paper_title: FRVT 2006 and ICE 2006 Large-Scale Experimental Results

paper_content:

This paper describes the large-scale experimental results from the Face Recognition Vendor Test (FRVT) 2006 and the Iris Challenge Evaluation (ICE) 2006. The FRVT 2006 looked at recognition from high-resolution still frontal face images and 3D face images, and measured performance for still frontal face images taken under controlled and uncontrolled illumination. The ICE 2006 evaluation reported verification performance for both left and right irises. The images in the ICE 2006 intentionally represent a broader range of quality than the ICE 2006 sensor would normally acquire. This includes images that did not pass the quality control software embedded in the sensor. The FRVT 2006 results from controlled still and 3D images document at least an order-of-magnitude improvement in recognition performance over the FRVT 2002. The FRVT 2006 and the ICE 2006 compared recognition performance from high-resolution still frontal face images, 3D face images, and the single-iris images. On the FRVT 2006 and the ICE 2006 data sets, recognition performance was comparable for high-resolution frontal face, 3D face, and the iris images. In an experiment comparing human and algorithms on matching face identity across changes in illumination on frontal face images, the best performing algorithms were more accurate than humans on unfamiliar faces.
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paper_title: Face Recognition Performance: Role of Demographic Information

paper_content:

This paper studies the influence of demographics on the performance of face recognition algorithms. The recognition accuracies of six different face recognition algorithms (three commercial, two nontrainable, and one trainable) are computed on a large scale gallery that is partitioned so that each partition consists entirely of specific demographic cohorts. Eight total cohorts are isolated based on gender (male and female), race/ethnicity (Black, White, and Hispanic), and age group (18-30, 30-50, and 50-70 years old). Experimental results demonstrate that both commercial and the nontrainable algorithms consistently have lower matching accuracies on the same cohorts (females, Blacks, and age group 18-30) than the remaining cohorts within their demographic. Additional experiments investigate the impact of the demographic distribution in the training set on the performance of a trainable face recognition algorithm. We show that the matching accuracy for race/ethnicity and age cohorts can be improved by training exclusively on that specific cohort. Operationally, this leads to a scenario, called dynamic face matcher selection, where multiple face recognition algorithms (each trained on different demographic cohorts) are available for a biometric system operator to select based on the demographic information extracted from a probe image. This procedure should lead to improved face recognition accuracy in many intelligence and law enforcement face recognition scenarios. Finally, we show that an alternative to dynamic face matcher selection is to train face recognition algorithms on datasets that are evenly distributed across demographics, as this approach offers consistently high accuracy across all cohorts.
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paper_title: Demographic classification: Do gender and ethnicity affect each other?

paper_content:

Gender and ethnicity classification are challenging topics in the field of face analysis. Some features, like skin color, are relevant only for ethnicity but not for gender; some others, like face geometry, are important for both. The impact of ethnicity in gender perception, as the effect of gender on ethnicity disambiguation, is not clear. This paper provides a study to check if gender and ethnicity affect each other during the classification. Three different well-established algorithms have been implemented to provide significant experiments. These algorithms are used for both gender and ethnicity classification. Ethnicity-specific gender classifiers are trained and tested using faces from a specific ethnicity; the accuracies achieved are compared with the ones obtained using generic gender classifiers, trained and tested with faces from different ethnic groups. With a similar procedure we compare gender-specific ethnicity classifiers, trained and tested selecting faces with a specific gender, with generic ethnicity classifiers, trained and tested with both male and female faces. The study shows that specific and generic classifiers perform equally. That means, for the features selected, gender and ethnicity do not affect each other.
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paper_title: Face age and sex modulate the other-race effect in face recognition

paper_content:

Faces convey a variety of socially relevant cues that have been shown to affect recognition, such as age, sex, and race, but few studies have examined the interactive effect of these cues. White participants of two distinct age groups were presented with faces that differed in race, age, and sex in a face recognition paradigm. Replicating the other-race effect, young participants recognized young own-race faces better than young other-race faces. However, recognition performance did not differ across old faces of different races (Experiments 1, 2A). In addition, participants showed an other-age effect, recognizing White young faces better than White old faces. Sex affected recognition performance only when age was not varied (Experiment 2B). Overall, older participants showed a similar recognition pattern (Experiment 3) as young participants, displaying an other-race effect for young, but not old, faces. However, they recognized young and old White faces on a similar level. These findings indicate that face cues interact to affect recognition performance such that age and sex information reliably modulate the effect of race cues. These results extend accounts of face recognition that explain recognition biases (such as the other-race effect) as a function of dichotomous ingroup/outgroup categorization, in that outgroup characteristics are not simply additive but interactively determine recognition performance.
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paper_title: Overview of the face recognition grand challenge

paper_content:

Over the last couple of years, face recognition researchers have been developing new techniques. These developments are being fueled by advances in computer vision techniques, computer design, sensor design, and interest in fielding face recognition systems. Such advances hold the promise of reducing the error rate in face recognition systems by an order of magnitude over Face Recognition Vendor Test (FRVT) 2002 results. The face recognition grand challenge (FRGC) is designed to achieve this performance goal by presenting to researchers a six-experiment challenge problem along with data corpus of 50,000 images. The data consists of 3D scans and high resolution still imagery taken under controlled and uncontrolled conditions. This paper describes the challenge problem, data corpus, and presents baseline performance and preliminary results on natural statistics of facial imagery.
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paper_title: An evaluation of multimodal 2D+3D face biometrics

paper_content:

We report on the largest experimental study to date in multimodal 2D+3D face recognition, involving 198 persons in the gallery and either 198 or 670 time-lapse probe images. PCA-based methods are used separately for each modality and match scores in the separate face spaces are combined for multimodal recognition. Major conclusions are: 1) 2D and 3D have similar recognition performance when considered individually, 2) combining 2D and 3D results using a simple weighting scheme outperforms either 2D or 3D alone, 3) combining results from two or more 2D images using a similar weighting scheme also outperforms a single 2D image, and 4) combined 2D+3D outperforms the multi-image 2D result. This is the first (so far, only) work to present such an experimental control to substantiate multimodal performance improvement.
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paper_title: Demographics versus Biometric Automatic Interoperability

paper_content:

Humans are naturally experts in recognizing faces. Such skills are enforced through a mix of cultural and cognitive processes. This allows human vision system to be especially efficient and effective in processing faces in a familiar environment. Automatic recognition system are currently not able (will ever be?) to achieve similar performance, especially when cross-demographic features are involved (gender, ethnicity, and age). Recent studies suggest a significant decrease of the number of recognition errors by limiting the search space to faces with the same demographics. This can be obtained by preliminarily annotating faces with a demographic profile, or by using demographic features as soft biometrics to be determined as a support to actual recognition. Especially in the second case, a multi-demographics dataset is needed to appropriately train a recognition system, and/or to test its performance. In this paper we use EGA dataset to test how interoperability relationships between biometric and demographics can be exploited for better recognition, though avoiding human intervention to preventively select appropriate demographic parameters.
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paper_title: A study of large-scale ethnicity estimation with gender and age variations

paper_content:

In this paper we study large-scale ethnicity estimation under variations of age and gender. The biologically-inspired features are applied to ethnicity classification for the first time. Through a large number of experiments on a large database with more than 21,000 face images, we systematically study the effect of gender and age variations on ethnicity estimation. Our finding is that ethnicity classification can have high accuracy in most cases, but an interesting phenomenon is observed that the ethnic classification accuracies could be reduced by 6∼8% in average when female faces are used for training while males for testing. The study results provide a guide for face processing on a multi-ethnic database, e.g., image collection from the Internet, and may inspire further psychological studies on ethnic grouping with gender and age variations. We also apply the methods to the whole MORPH-II database with more than 55,000 face images for ethnicity classification of five races. It is the first time that ethnicity estimation is performed on so large a database.
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paper_title: Evidence and a Computational Explanation of Cultural Differences in Facial Expression Recognition

paper_content:

Facial expressions are crucial to human social communication, but the extent to which they are innate and universal versus learned and culture dependent is a subject of debate. Two studies explored the effect of culture and learning on facial expression understanding. In Experiment 1, Japanese and U.S. participants interpreted facial expressions of emotion. Each group was better than the other at classifying facial expressions posed by members of the same culture. In Experiment 2, this reciprocal in-group advantage was reproduced by a neurocomputational model trained in either a Japanese cultural context or an American cultural context. The model demonstrates how each of us, interacting with others in a particular cultural context, learns to recognize a culture-specific facial expression dialect.
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paper_title: Ethnic Categorisation of Faces is Not Independent of Face Identity

paper_content:

Is the extraction of a visually derived semantic code from faces (ethnicity) affected by face identity (familiarity) or not? The traditional view considers that this operation is made independently of face identity, and in parallel with the recognition of identity. However, some recent studies cast doubt on this parallel thesis regarding other visually derived semantic codes, namely: facial expression, facial speech, apparent age, and gender. Twenty-eight Caucasian participants were enrolled in an 'ethnic-decision' task on morphed faces made of an Asiatic source face and a Caucasian source face, in the proportion of 70%-30%. Half of the original faces were previously made familiar by a learning procedure (associating the face, surname, occupation, and city of residence of the person displayed), while the remaining half were unfamiliar. The results showed clearly that ethnic decision was affected by face familiarity. This adds support to the thesis according to which the identification of identity and the extraction of visually derived semantic codes are not made independently from each other and that the 'parallel-route' hypothesis becomes weakly supported.
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paper_title: Face recognition algorithms and the other-race effect : computational mechanisms for a developmental contact hypothesis

paper_content:

People recognize faces of their own race more accurately than faces of other races. The “contact” hypothesis suggests that this “other-race effect” occurs as a result of the greater experience we have with own- versus other-race faces. The computational mechanisms that may underlie different versions of the contact hypothesis were explored in this study. We replicated the other-race effect with human participants and evaluated four classes of computational face recognition algorithms for the presence of an other-race effect. Consistent with the predictions of a developmentalcontact hypothesis, “experience-based models” demonstrated an other-race effect only when the representational system was developed through experience that warped the perceptual space in a way that was sensitive to the overall structure of the model’s experience with faces of different races. When the model’s representation relied on a feature set optimized to encode the information in the learned faces, experience-based algorithms recognized minority-race faces more accurately than majority-race faces. The results suggest a developmental learning process that warps the perceptual space to enhance the encoding of distinctions relevant for own-race faces. This feature space limits the quality of face representations for other-race faces.
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paper_title: Simulating the "other-Race Effect" as a Problem in Perceptual Learning

paper_content:

Abstract We report a series of simulations on the well-known ‘other-race effect’. We trained an autoassociative network on a majority and a minority race of faces, and tested the model's ability to process faces from the two races in different ways. First, the model was better able to reconstruct unlearned majority faces than minority faces. Secondly, the average inter-face similarity was higher for the reconstructed minority faces than for reconstructed majority faces, indicating that the model was coding the majority faces more distinctively than the minority faces. These results held for Caucasian faces as the majority race and Japanese faces as the minority race and vice versa. Thirdly, we simulated a recognition task for same- and other-race faces by using a face history matrix and a recognition task matrix with equal numbers of Caucasian and Japanese faces, and reconstructing these faces as a weighted combination of the two matrices. Using Caucasian faces as the majority race, the model was better a...
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paper_title: A survey of approaches and challenges in 3D and multi-modal 3D + 2D face recognition

paper_content:

This survey focuses on recognition performed by matching models of the three-dimensional shape of the face, either alone or in combination with matching corresponding two-dimensional intensity images. Research trends to date are summarized, and challenges confronting the development of more accurate three-dimensional face recognition are identified. These challenges include the need for better sensors, improved recognition algorithms, and more rigorous experimental methodology.
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paper_title: EMPATH: A Neural Network that Categorizes Facial Expressions

paper_content:

There are two competing theories of facial expression recognition. Some researchers have suggested that it is an example of "categorical perception." In this view, expression categories are considered to be discrete entities with sharp boundaries, and discrimination of nearby pairs of expressive faces is enhanced near those boundaries. Other researchers, however, suggest that facial expression perception is more graded and that facial expressions are best thought of as points in a continuous, low-dimensional space, where, for instance, "surprise" expressions lie between "happiness" and "fear" expressions due to their perceptual similarity. In this article, we show that a simple yet biologically plausible neural network model, trained to classify facial expressions into six basic emotions, predicts data used to support both of these theories. Without any parameter tuning, the model matches a variety of psychological data on categorization, similarity, reaction times, discrimination, and recognition difficulty, both qualitatively and quantitatively. We thus explain many of the seemingly complex psychological phenomena related to facial expression perception as natural consequences of the tasks' implementations in the brain.
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paper_title: A multidimensional scaling analysis of own- and cross-race face spaces

paper_content:

Abstract We examined predictions derived from Valentine’s (1991) Multidimensional Space (MDS) framework for own- and other-race face processing. A set of 20 computerized faces was generated from a single prototype. Each face was saved as Black and White, changing only skin tone, such that structurally identical faces were represented in both race categories. Participants made speeded “same-different” judgments to all possible combinations of faces, from which we generated psychological spaces, with “different” RTs as the measure of similarity. Consistent with the MDS framework, all faces were pseudo-normally distributed around the (unseen) prototype. The distribution of faces was consistent with Valentine’s (1991) predictions: despite their physical identity to the White faces, Black faces had lower mean inter-object distances in psychological space. Other-race faces are more densely clustered in psychological space, which could underlie well-known recognition deficits.
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paper_title: Deep Hierarchies in the Primate Visual Cortex: What Can We Learn for Computer Vision?

paper_content:

Computational modeling of the primate visual system yields insights of potential relevance to some of the challenges that computer vision is facing, such as object recognition and categorization, motion detection and activity recognition, or vision-based navigation and manipulation. This paper reviews some functional principles and structures that are generally thought to underlie the primate visual cortex, and attempts to extract biological principles that could further advance computer vision research. Organized for a computer vision audience, we present functional principles of the processing hierarchies present in the primate visual system considering recent discoveries in neurophysiology. The hierarchical processing in the primate visual system is characterized by a sequence of different levels of processing (on the order of 10) that constitute a deep hierarchy in contrast to the flat vision architectures predominantly used in today's mainstream computer vision. We hope that the functional description of the deep hierarchies realized in the primate visual system provides valuable insights for the design of computer vision algorithms, fostering increasingly productive interaction between biological and computer vision research.
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paper_title: Learning a Dictionary of Shape-Components in Visual Cortex: Comparison with Neurons, Humans and Machines

paper_content:

In this thesis, I describe a quantitative model that accounts for the circuits and computations of the feedforward path of the ventral stream of visual cortex. This model is consistent with a general theory of visual processing that extends the hierarchical model of [Hubel and Wiesel, 1959] from primary to extrastriate visual areas. It attempts to explain the first few hundred milliseconds of visual processing and "immediate recognition". One of the key elements in the approach is the learning of a generic dictionary of shape-components from V2 to IT, which provides an invariant representation to task-specific categorization circuits in higher brain areas. This vocabulary of shape-tuned units is learned in an unsupervised manner from natural images, and constitutes a large and redundant set of image features with different complexities and invariances. This theory significantly extends an earlier approach by [Riesenhuber and Poggio, 1999a] and builds upon several existing neurobiological models and conceptual proposals. ::: First, I present evidence to show that the model can duplicate the tuning properties of neurons in various brain areas (e.g., V1, V4 and IT). In particular, the model agrees with data from V4 about the response of neurons to combinations of simple two-bar stimuli [Reynolds et al., 1999] (within the receptive field of the S2 units) and some of the C2 units in the model show a tuning for boundary conformations which is consistent with recordings from V4 [Pasupathy and Connor, 2001]. Second, I show that not only can the model duplicate the tuning properties of neurons in various brain areas when probed with artificial stimuli, but it can also handle the recognition of objects in the real-world, to the extent of competing with the best computer vision systems. Third, I describe a comparison between the performance of the model and the performance of human observers in a rapid animal vs. non-animal recognition task for which recognition is fast and cortical back-projections are likely to be inactive. Results indicate that the model predicts human performance extremely well when the delay between the stimulus and the mask is about 50 ms. This suggests that cortical back-projections may not play a significant role when the time interval is in this range, and the model may therefore provide a satisfactory description of the feedforward path. ::: Taken together, the evidences suggest that we may have the skeleton of a successful theory of visual cortex. In addition, this may be the first time that a neurobiological model, faithful to the physiology and the anatomy of visual cortex, not only competes with some of the best computer vision systems thus providing a realistic alternative to engineered artificial vision systems, but also achieves performance close to that of humans in a categorization task involving complex natural images. (Copies available exclusively from MIT Libraries, Rm. 14-0551, Cambridge, MA 02139-4307. Ph. 617-253-5668; Fax 617-253-1690.)
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paper_title: State-of-the-Art in Visual Attention Modeling

paper_content:

Modeling visual attention-particularly stimulus-driven, saliency-based attention-has been a very active research area over the past 25 years. Many different models of attention are now available which, aside from lending theoretical contributions to other fields, have demonstrated successful applications in computer vision, mobile robotics, and cognitive systems. Here we review, from a computational perspective, the basic concepts of attention implemented in these models. We present a taxonomy of nearly 65 models, which provides a critical comparison of approaches, their capabilities, and shortcomings. In particular, 13 criteria derived from behavioral and computational studies are formulated for qualitative comparison of attention models. Furthermore, we address several challenging issues with models, including biological plausibility of the computations, correlation with eye movement datasets, bottom-up and top-down dissociation, and constructing meaningful performance measures. Finally, we highlight current research trends in attention modeling and provide insights for future.
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paper_title: A Model of Saliency-Based Visual Attention for Rapid Scene Analysis

paper_content:

A visual attention system, inspired by the behavior and the neuronal architecture of the early primate visual system, is presented. Multiscale image features are combined into a single topographical saliency map. A dynamical neural network then selects attended locations in order of decreasing saliency. The system breaks down the complex problem of scene understanding by rapidly selecting, in a computationally efficient manner, conspicuous locations to be analyzed in detail. Index Terms—Visual attention, scene analysis, feature extraction, target detection, visual search. ———————— F ————————
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paper_title: Training Hierarchical Feed-Forward Visual Recognition Models Using Transfer Learning from Pseudo-Tasks

paper_content:

Building visual recognition models that adapt across different domains is a challenging task for computer vision. While feature-learning machines in the form of hierarchial feed-forward models (e.g., convolutional neural networks) showed promise in this direction, they are still difficult to train especially when few training examples are available. In this paper, we present a framework for training hierarchical feed-forward models for visual recognition, using transfer learning from pseudo tasks. These pseudo tasks are automatically constructed from data without supervision and comprise a set of simple pattern-matching operations. We show that these pseudo tasks induce an informative inverse-Wishart prior on the functional behavior of the network, offering an effective way to incorporate useful prior knowledge into the network training. In addition to being extremely simple to implement, and adaptable across different domains with little or no extra tuning, our approach achieves promising results on challenging visual recognition tasks, including object recognition, gender recognition, and ethnicity recognition.
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paper_title: Visual object understanding

paper_content:

Visual object understanding includes processes at the nexus of visual perception and visual cognition. A traditional approach separates questions that are more associated with perception — how are objects represented by high-level vision — from questions that are more associated with cognition — how are objects identified, categorized and remembered. However, to understand the bridge between perception and cognition, it is fruitful to abandon any sharp distinction between perceptual and cognitive aspects of visual object understanding. We provide a selective review of research from both the Object Recognition and Perceptual Categorization literatures, highlighting relevant behavioural, neuropsychological, neurophysiological and theoretical research into the representations and processes that underlie visual object understanding in humans and primates.
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paper_title: A Spiking Neural Network Based Cortex-Like Mechanism and Application to Facial Expression Recognition

paper_content:

In this paper, we present a quantitative, highly structured cortex-simulated model, which can be simply described as feedforward, hierarchical simulation of ventral stream of visual cortex using biologically plausible, computationally convenient spiking neural network system. The motivation comes directly from recent pioneering works on detailed functional decomposition analysis of the feedforward pathway of the ventral stream of visual cortex and developments on artificial spiking neural networks (SNNs). By combining the logical structure of the cortical hierarchy and computing power of the spiking neuron model, a practical framework has been presented. As a proof of principle, we demonstrate our system on several facial expression recognition tasks. The proposed cortical-like feedforward hierarchy framework has the merit of capability of dealing with complicated pattern recognition problems, suggesting that, by combining the cognitive models with modern neurocomputational approaches, the neurosystematic approach to the study of cortex-like mechanism has the potential to extend our knowledge of brain mechanisms underlying the cognitive analysis and to advance theoretical models of how we recognize face or, more specifically, perceive other people's facial expression in a rich, dynamic, and complex environment, providing a new starting point for improved models of visual cortex-like mechanism.
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paper_title: Deficits in cross-race face learning: insights from eye-movements and pupillometry

paper_content:

The own-race bias (ORB) is a well-known finding wherein people are better able to recognize and discriminate own-race faces, relative to cross-race faces. In 2 experiments, participants viewed Asian and Caucasian faces, in preparation for recognition memory tests, while their eye movements and pupil diameters were continuously monitored. In Experiment 1 (with Caucasian participants), systematic differences emerged in both measures as a function of depicted race: While encoding cross-race faces, participants made fewer (and longer) fixations, they preferentially attended to different sets of features, and their pupils were more dilated, all relative to own-race faces. Also, in both measures, a pattern emerged wherein some participants reduced their apparent encoding effort to cross-race faces over trials. In Experiment 2 (with Asian participants), the authors observed the same patterns, although the ORB favored the opposite set of faces. Taken together, the results suggest that the ORB appears during initial perceptual encoding. Relative to own-race face encoding, cross-race encoding requires greater effort, which may reduce vigilance in some participants.
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paper_title: Eyetracking and selective attention in category learning

paper_content:

An eyetracking version of the classic Shepard, Hovland, and Jenkins (1961) experiment was conducted. Forty years of research has assumed that category learning often involves learning to selectively attend to only those stimulus dimensions useful for classification. We confirmed that participants learned to allocate their attention optimally. We also found that learners tend to fixate all stimulus dimensions early in learning. This result obtained despite evidence that participants were also testing one-dimensional rules during this period. Finally, the restriction of eye movements to only relevant dimensions tended to occur only after errors were largely (or completely) eliminated. We interpret these findings as consistent with multiple-systems theories of learning which maximize information input in order to maximize the number of learning modules involved, and which focus solely on relevant information only after one module has solved the learning problem.
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paper_title: Attending to Threat: Race-based Patterns of Selective Attention

paper_content:

The present research investigated the extent to which the stereotype that young Black men are threatening and dangerous has become so robust and ingrained in the collective American unconscious that Black men now capture attention, much like evolved threats such as spiders and snakes. Specifically, using a dot-probe detection paradigm, White participants revealed biased attention toward Black faces relative to White faces (Study 1). Because the faces were presented only briefly (30-ms), the bias is thought to reflect the early engagement of attention. The attentional bias was eliminated, however, when the faces displayed averted eye-gaze (Study 2). That is, when the threat communicated by the Black faces was attenuated by a relevant, competing socio-emotional cue—in this case, averted eye-gaze—they no longer captured perceivers’ attention. Broader implications for social cognition, as well as public policies that reify these prevailing perceptions of young Black men are discussed.
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paper_title: Computational modelling of visual attention

paper_content:

Five important trends have emerged from recent work on computational models of focal visual attention that emphasize the bottom-up, image-based control of attentional deployment. First, the perceptual saliency of stimuli critically depends on the surrounding context. Second, a unique 'saliency map' that topographically encodes for stimulus conspicuity over the visual scene has proved to be an efficient and plausible bottom-up control strategy. Third, inhibition of return, the process by which the currently attended location is prevented from being attended again, is a crucial element of attentional deployment. Fourth, attention and eye movements tightly interplay, posing computational challenges with respect to the coordinate system used to control attention. And last, scene understanding and object recognition strongly constrain the selection of attended locations. Insights from these five key areas provide a framework for a computational and neurobiological understanding of visual attention.
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paper_title: Emotion processing and the amygdala: from a 'low road' to 'many roads' of evaluating biological significance

paper_content:

A subcortical pathway through the superior colliculus and pulvinar to the amygdala is commonly assumed to mediate the non-conscious processing of affective visual stimuli. We review anatomical and physiological data that argue against the notion that such a pathway plays a prominent part in processing affective visual stimuli in humans. Instead, we propose that the primary role of the amygdala in visual processing, like that of the pulvinar, is to coordinate the function of cortical networks during evaluation of the biological significance of affective visual stimuli. Under this revised framework, the cortex has a more important role in emotion processing than is traditionally assumed.
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paper_title: Visual features of intermediate complexity and their use in classification

paper_content:

The human visual system analyzes shapes and objects in a series of stages in which stimulus features of increasing complexity are extracted and analyzed. The first stages use simple local features, and the image is subsequently represented in terms of larger and more complex features. These include features of intermediate complexity and partial object views. The nature and use of these higher-order representations remains an open question in the study of visual processing by the primate cortex. Here we show that intermediate complexity (IC) features are optimal for the basic visual task of classification. Moderately complex features are more informative for classification than very simple or very complex ones, and so they emerge naturally by the simple coding principle of information maximization with respect to a class of images. Our findings suggest a specific role for IC features in visual processing and a principle for their extraction.
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paper_title: Perceptions of race

paper_content:

Until recently, experiments on person perception had led to two unwelcome conclusions: (1) people encode the race of each individual they encounter, and (2) race encoding is caused by computational mechanisms whose operation is automatic and mandatory. Evolutionary analyses rule out the hypothesis that the brain mechanisms that cause race encoding evolved for that purpose. Consequently, race encoding must be a byproduct of mechanisms that evolved for some alternative function. But which one? Race is not encoded as a byproduct of domain-general perceptual processes. Two families of byproduct hypotheses remain: one invokes inferential machinery designed for tracking coalitional alliances, the other machinery designed for reasoning about natural kinds. Recent experiments show that manipulating coalitional variables can dramatically decrease the extent to which race is noticed and remembered.
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paper_title: Neocognitron: A Self-Organizing Neural Network Model for a Mechanism of Visual Pattern Recognition

paper_content:

A neural network model, called a “neocognitron”, is proposed for a mechanism of visual pattern recognition. It is demonstrated by computer simulation that the neocognitron has characteristics similar to those of visual systems of vertebrates.
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paper_title: Evidence for hypodescent and racial hierarchy in the categorization and perception of biracial individuals.

paper_content:

Individuals who qualify equally for membership in two racial groups provide a rare window into social categorization and perception. In 5 experiments, we tested the extent to which a rule of hypodescent, whereby biracial individuals are assigned the status of their socially subordinate parent group, would govern perceptions of Asian–White and Black–White targets. In Experiment 1, in spite of posing explicit questions concerning Asian–White and Black–White targets, hypodescent was observed in both cases and more strongly in Black–White social categorization. Experiments 2A and 2B used a speeded response task and again revealed evidence of hypodescent in both cases, as well as a stronger effect in the Black–White target condition. In Experiments 3A and 3B, social perception was studied with a face-morphing task. Participants required a face to be lower in proportion minority to be perceived as minority than in proportion White to be perceived as White. Again, the threshold for being perceived as White was higher for Black–White than for Asian–White targets. An independent categorization task in Experiment 3B further confirmed the rule of hypodescent and variation in it that reflected the current racial hierarchy in the United States. These results documenting biases in the social categorization and perception of biracials have implications for resistance to change in the American racial hierarchy.
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paper_title: Can computers cope with human races?

paper_content:

In trying to apply a computer to a task that humans do, we often discover that it doesn't work. One common problem is that humans are able to deal with fuzzy concepts, but computers are not—they need precise representations and it is hard to represent a fuzzy concept in a precise way. However, if we look closer at such tasks, we often discover that the weakness actually lies not in the computer but in ourselves—that we didn't understand what we were doing in the first place. When faced with a problem of this sort, some people refuse to recognize the conceptual failure. Instead of seeking a better representation for the task, they thrash away at making the fuzzy scheme work, insisting that there is nothing wrong with the conceptual base. I will illustrate one such problem with a true story. The central theme is the fuzzy concept of racial and ethnic classification, as used by the U.S. government and a horde of other bureaucracies. These organizations have been carrying out elaborate statistical computations and making major policy decisions based on this concept for many years with problematical results. I begin with my first encounter with this scheme, some 25 years ago.
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paper_title: Natural ambiguities: Racial categorization of multiracial individuals

paper_content:

article i nfo Article history: Understanding the perception of multiracial persons is increasingly important in today's diverse society. The present research investigated the process of categorizing multiracial persons as "Multiracial." We hypothe- sized that perceivers would make fewer Multiracial categorizations of multiracials and that these categoriza- tions would take longer than monoracial categorizations. We found support for these hypotheses across six experiments. Experiment 1 demonstrated that perceivers did not categorize morphed Black-White faces as Multiracial with the same frequency with which they categorized Black and White faces as Black and White (respectively), and that categorizations of multiracials as Multiracial took longer than monoracial cat- egorizations. Experiment 2 replicated and extended these effects to real Black-White faces. Experiment 3 showed that these findings generalized to Asian-White faces. We used pixel variance analysis to show that these effects were not due to increased variance among Multiracial faces. The image analysis showed that the Black-White morphs and real biracials were actually less varied than either the Black or White sets of faces. Experiments 4 and 5 demonstrated that cognitive load and time constraints detrimentally affected multiracial, but not monoracial, categorizations. Experiment 6 showed that imbuing monoracial categories with importance decreases the use of the Multiracial category. Implications of these findings for understand- ing perceptions of multiracial persons are discussed.
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paper_title: Categorization of humans in biomedical research: genes, race and disease

paper_content:

A debate has arisen regarding the validity of racial/ethnic categories for biomedical and genetic research. An epidemiologic perspective on the issue of human categorization in biomedical and genetic research strongly supports the continued use of self-identified race and ethnicity.
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Title: Learning Race from Face: A Survey

Section 1: INTRODUCTION
Description 1: This section provides an overview of the key motivations behind race recognition from facial features, the challenges faced, and the objectives of the survey.

Section 2: HUMAN RACE PERCEPTION FORMULATION
Description 2: This section explores the conceptual understanding of race and how it is perceived from an interdisciplinary perspective, including psychology, cognitive neuroscience, and computer vision.

Section 3: Race and Other-Race Effect (ORE)
Description 3: This section discusses the Other-Race Effect (ORE), explaining how human observers perform better at recognizing faces of their own race compared to other races.

Section 4: Race and Physical Anthropometry
Description 4: This section details the role of physical anthropometry in distinguishing races and the statistical significance of different facial features among race groups.

Section 5: Race and Cognitive Neuroscience
Description 5: This section looks into how race perception is processed and evaluated by the brain, supported by studies in cognitive neuroscience using tools like fMRI, ERP, and EEG.

Section 6: Racial Feature Representation
Description 6: This section reviews the techniques for representing racial features in facial images, including chromatic, global, and local feature representations, and combination methods.

Section 7: RACE CLASSIFICATION: STATE-OF-THE-ART
Description 7: This section provides a comprehensive review of the state-of-the-art research on race recognition, covering single-model recognition, 3D face analysis, and intra-race recognition methods.

Section 8: RACIAL FACE DATABASES
Description 8: This section examines the existing racial face databases, their characteristics, and the importance of having representative and diverse datasets for evaluating race recognition algorithms.

Section 9: RACE RECOGNITION: REAL-WORLD APPLICATIONS
Description 9: This section highlights various real-world applications of race recognition, such as biometric identification, video surveillance, public safety, criminal judgment, forensic art, and human-computer interaction.

Section 10: What Have We Learned So Far?
Description 10: This section summarizes the key findings from the survey, including insights on race categorization, the role of anthropometry, and the development of racially diverse face databases.

Section 11: What Could We Do Next?
Description 11: This section discusses future research directions to improve race recognition, such as real-world learning, multi-modality frameworks, manifold learning, and more.

Section 12: New Frontier: Emerging Opportunities and Challenges
Description 12: This section focuses on the future development of racial face recognition, identifying emerging opportunities and challenges in the field, and suggestions for advancing the research.

Section 13: CONCLUSIONS
Description 13: This section concludes the survey, recapping the major contributions, and discussing the potential impact on future research and applications in race recognition.

Section 14: ACKNOWLEDGMENTS
Description 14: This section acknowledges the funding sources and contributions of individuals and institutions that supported the research and development of the survey.