A model of the perception of facial expressions of emotion by humans: Research overview and perspectives
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paper_title: Nonmanuals, semantic operators, domain marking, and the solution to two outstanding puzzles in ASL

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This paper provides an analysis of certain nonmanuals from a semantic perspective with respect to the different types of semantic operators they are associated with. The categories of operators include simple/monadic and tripartite/dyadic. This semantic analysis will explain different phonological spreading among upper face/head nonmanuals: negative headshake, brow lowering, and structurally varied use of brow raise. Negative headshake and [+wh]-question brow lowering spread over their c-command domain. However, brow raise does not spread over its c-command domain, and its spreading domain is harder to characterize. The operator analysis provides a coherent explanation for the spreading domain. This distinction underlies a new analysis of the derived sign understand′, and helps resolve two puzzling issues related to its use: (1) why it has brow raise; and (2) what position it occupies in CP. This solution offers additional evidence in support of the claim that ASL has the spec,CP on the left.
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paper_title: The society of mind

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Marvin Minsky--one of the fathers of computer science and cofounder of the Artificial Intelligence Laboratory at MIT--gives a revolutionary answer to the age-old question: "how does the mind work?"
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paper_title: Looking at people: sensing for ubiquitous and wearable computing

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The research topic of looking at people, that is, giving machines the ability to detect, track, and identify people and more generally, to interpret human behavior, has become a central topic in machine vision research. Initially thought to be the research problem that would be hardest to solve, it has proven remarkably tractable and has even spawned several thriving commercial enterprises. The principle driving application for this technology is "fourth generation" embedded computing: "smart" environments and portable or wearable devices. The key technical goals are to determine the computer's context with respect to nearby humans (e.g., who, what, when, where, and why) so that the computer can act or respond appropriately without detailed instructions. The paper examines the mathematical tools that have proven successful, provides a taxonomy of the problem domain, and then examines the state of the art. Four areas receive particular attention: person identification, surveillance/monitoring, 3D methods, and smart rooms/perceptual user interfaces. Finally, the paper discusses some of the research challenges and opportunities.
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paper_title: Emotion Theory and Research: Highlights, Unanswered Questions, and Emerging Issues

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Emotion feeling is a phase of neurobiological activity, the key component of emotions and emotion-cognition interactions. Emotion schemas, the most frequently occurring emotion experiences, are dynamic emotion-cognition interactions that may consist of momentary/situational responding or enduring traits of personality that emerge over developmental time. Emotions play a critical role in the evolution of consciousness and the operations of all mental processes. Types of emotion relate differentially to types or levels of consciousness. Unbridled imagination and the ability for sympathetic regulation of empathy may represent both potential gains and losses from the evolution and ontogeny of emotion processes and consciousness. Unresolved issues include psychology's neglect of levels of consciousness that are distinct from access or reflective consciousness and use of the term “unconscious mind” as a dumpster for all mental processes that are considered unreportable. The relation of memes and the mirror neur...
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paper_title: Identification and ratings of caricatures: Implications for mental representations of faces

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Abstract S. Brennan (1985, Leonardo , 18 , 170–178) has developed a computer-implemented caricature generator based on a holistic theory of caricature. A face is represented by 37 lines, based on a fixed set of 169 points. Caricatures are produced by exaggerating all metric differences between a face and a norm. Anticaricatures can be created by reducing all the differences between a face and a norm. Caricatures of familiar faces were identified more quickly than veridical line drawings, which were identified more quickly than anticaricatures. There was no difference in identification accuracy for the three types of representation. The best likeness was considered to be a caricature. We discuss the implications of these results for how faces are mentally represented. The results are consistent with a holistic theory of encoding in which distinctive aspects of a face are represented by comparison with a norm. We suggest that this theory may be appropriate for classes of visual stimuli, other than faces, whose members share a configuration definable by a fixed set of points.
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paper_title: A unified account of the effects of distinctiveness, inversion, and race in face recognition

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A framework is outlined in which individual faces are assumed to be encoded as a point in a multidimensional space, defined by dimensions that serve to discriminate faces. It is proposed that such a framework can account for the effects of distinctiveness, inversion, and race on recognition of faces. Two specific models within this framework are identified: a norm-based coding model, in which faces are encoded as vectors from a population norm or prototype; and a purely exemplar-based model. Both models make similar predictions, albeit in different ways, concerning the interactions between the effects of distinctiveness, inversion and race. These predictions were supported in five experiments in which photographs of faces served as stimuli. The norm-based coding version and the exemplar-based version of the framework cannot be distinguished on the basis of the experiments reported, but it is argued that a multidimensional space provides a useful heuristic framework to investigate recognition of faces. Fin...
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paper_title: What the face reveals : basic and applied studies of spontaneous expression using the facial action coding system (FACS)

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Foreword Introduction The study of spontaneous facial expression in psychology I: BASIC RESEARCH ON EMOTION 1. Is the startle reaction an emotion? 2. The asymmetry of facial actions is inconsisten with models of hemispheric specialization 3. Coherence between expressive and experiential systmes in emotion 4. Will the real relationship between facial expression and affective experience please stand up: The case of exhilaration 5. Extraversion, alcohol, and enjoyment 6. Signs of appeasement: Evidence for the distinct displays of embarrassment, amusement, and shame 7. Genuine, suppressed, and faked facial behavior during exacerbation of chronic low back pain 8. The consistency of facial expressions of pain: a comparison across modalities 9. Smiles when lying 10. Behavioral markers and recognizability of the smile of enjoyment 11. Components and recognition of facial expression in the communication of emotion by actors 12. Differentiating emotion elicited and deliberate emotional facial expressions 13. Japanese and American infants' responses to arm restraint 14. Differential facial responses to four basic tests in newborns II: APPLIED RESEARCH 15. Facial expressions in affective disorders 16. Emotional experience and epxression in schizophrenia and depression 17. Interaction regulations used by schizophrenic and psychosomatic patients: Studies on facial behavior in dyadic interactions 18. Nonverbal expression of psychological states in psychiatric patients 19. Depression and suicide faces 20. Prototypical affective microsequences in psychotherapeutic interaction 21. Facial expressions of emotion and psychopathology in adolescent boys 22. Type A behavior pattern: Facial behavior and speech components Conclusion What we have learned by measuring the face Index
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paper_title: Categorical effects in the perception of faces

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Abstract These studies suggest categorical perception effects may be much more general than has commonly been believed and can occur in apparently similar ways at dramatically different levels of processing. To test the nature of individual face representations, a linear continuum of “morphed” faces was generated between individual exemplars of familiar faces. In separate categorization, discrimination and “better-likeness” tasks, subjects viewed pairs of faces from these continua. Subjects discriminate most accurately when face-pairs straddle apparent category boundaries; thus individual faces are perceived categorically. A high correlation is found between the familiarity of a face-pair and the magnitude of the categorization effect. Categorical perception therefore is not limited to low-level perceptual continua, but can occur at higher levels and may be acquired through experience as well.
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paper_title: Core affect and the psychological construction of emotion

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At the heart of emotion, mood, and any other emotionally charged event are states experienced as simply feeling good or bad, energized or enervated. These states— called core affect—influence reflexes, perception, cognition, and behavior and are influenced by many causes internal and external, but people have no direct access to these causal connections. Core affect can therefore be experienced as freefloating (mood) or can be attributed to some cause (and thereby begin an emotional episode). These basic processes spawn a broad framework that includes perception of the core-affect-altering properties of stimuli, motives, empathy, emotional meta-experience, and affect versus emotion regulation; it accounts for prototypical emotional episodes, such as fear and anger, as core affect attributed to something plus various nonemotional processes. Most major topics in psychology and every major problem faced by humanity involve emotion. Perhaps the same could be said of cognition. Yet, in the psychology of human beings, with passions as well as reasons, with feelings as well as thoughts, it is the emotional side that remains the more mysterious. Psychology and humanity can progress without considering emotion—about as fast as someone running on one leg. I hope to clear away some of the mystery in the study of emotion by advancing a conceptual framework, pitched at a psychological level, for emotion and all other emotionally charged occurrent events. Admittedly, few psychologists see any need for a new framework because we already have one: The concept of emotion defines the field by specifying, however vaguely, the set of events that any theory of this topic is required to explain. A list of everyday words such as fear, anger, and happiness divides emotion into categories—and there begins a periodic table of emotional atoms. The concepts of emotion, fear, anger, and so forth are parts of a folk theory inherited from human shepherding and farming ancestors. These concepts have long provided predictions and understanding and are now a part of common sense. They are embedded in psychologists’ questions and, as a consequence, answers. They guided psychology’s initial theories on this topic. Yet the result has been called a “very confused and confusing field of study” (Ortony, Clore, & Collins, 1988, p. 2). Continuing deep controversies on fundamental questions (e.g., Lazarus, 1982; Ortony & Turner, 1990; Zajonc, 1980) have suggested that these guideposts might not have pointed in the best direction. They might be one of the sources of the mystery. People’s intuitive ideas about their own psychological processes need not be wrong, but they need not be right either. Some folk concepts are eventually forged into scientific tools; but others are too local or ill conceived for scientific purposes. Each folk concept must be scrutinized for its scientific usefulness. Every generation of psychologists has seen a prominent and respected writer come to question the scientific credentials of emotion concepts (especially emotion itself, a word for which does not exist in all languages; Russell, 1991) and the hidden assumptions that underlie them. LeDoux (1996) found no neurological basis for grouping various processes together as emo
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paper_title: A Theory of Emotion, and its Application to Understanding the Neural Basis of Emotion

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Abstract It is shown that emotions can usefully be considered as states produced by reinforcing stimuli. The ways in which a wide variety of emotions can be produced, and the functions of emotion, are considered. There is evidence that the amygdala is involved in the formation of stimulus-reinforcement associations, and the orbitofrontal cortex with correcting behavioural responses when these are no longer appropriate because previous reinforcement contingencies change. This evidence comes from the effects of damage to these structures, and from recording the activity of single neurons in these structures in the monkey during the formation and disconnection of stimulus-reinforcement associations. In so far as emotions can be defined as states produced by reinforcing stimuli, then the amygdala and orbitofrontal cortex are seen to be of great importance for emotions, in that they are involved respectively in the elicitation of learned emotional responses, and in the correction or adjustment of these emotion...
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paper_title: The resolution of facial expressions of emotion

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Much is known on how facial expressions of emotion are produced, including which individual muscles are most active in each expression. Yet, little is known on how this information is interpreted by the human visual system. This paper presents a systematic study of the image dimensionality of facial expressions of emotion. In particular, we investigate how recognition degrades when the resolution of the image (i.e., number of pixels when seen as a 5.3 by 8 degrees stimulus) is reduced. We show that recognition is only impaired in practise when the image resolution goes below 20×30 pixels. A study of the confusion tables demonstrates that each expression of emotion is consistently confused by a small set of alternatives and that the confusion is not symmetric, i.e., misclassifying emotion a as b does not imply we will mistake b for a. This asymmetric pattern is consistent over the different image resolutions and cannot be explained by the similarity of muscle activation. Furthermore, although women are generally better at recognizing expressions of emotion at all resolutions, the asymmetry patterns are the same. We discuss the implications of these results for current models of
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paper_title: Computer-enhanced emotion in facial expressions

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Benson and Perrett's (1991b) computer–based caricature procedure was used to alter the positions of anatomical landmarks in photographs of emotional facial expressions with respect to their locations in a reference norm face (e.g. a neutral expression). Exaggerating the differences between an expression and its norm produces caricatured images, whereas reducing the differences produces ‘anti–caricatures’. Experiment 1 showed that caricatured (+50 % different from neutral) expressions were recognized significantly faster than the veridical (0 %, undistorted) expressions. This held for all six basic emotions from the Ekman and Friesen (1976) series, and the effect generalized across different posers. For experiment 2, caricatured (+50 %) and anti–caricatured (−50 %) images were prepared using two types of references norm; a neutral–expression norm, which woud be optimal if facial expression recognition involves monitoring changes in the positioning of underlying facial muscles (excluding neutral) in the Ekman & Friesen (1976) series. The results showed that the caricatured images were identified significantly faster, and the anti–caricatured images significantly slower, than the veridical expressions. Furthermore, the neutral–expression and average–expression norm caricatures produced the same pattern of results.
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paper_title: NEUROPSYCHOLOGY OF FEAR AND LOATHING

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For over 60 years, ideas about emotion in neuroscience and psychology have been dominated by a debate on whether emotion can be encompassed within a single, unifying model. In neuroscience, this approach is epitomized by the limbic system theory and, in psychology, by dimensional models of emotion. Comparative research has gradually eroded the limbic model, and some scientists have proposed that certain individual emotions are represented separately in the brain. Evidence from humans consistent with this approach has recently been obtained by studies indicating that signals of fear and disgust are processed by distinct neural substrates. We review this research and its implications for theories of emotion.
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paper_title: Emotion Theory and Research: Highlights, Unanswered Questions, and Emerging Issues

paper_content:

Emotion feeling is a phase of neurobiological activity, the key component of emotions and emotion-cognition interactions. Emotion schemas, the most frequently occurring emotion experiences, are dynamic emotion-cognition interactions that may consist of momentary/situational responding or enduring traits of personality that emerge over developmental time. Emotions play a critical role in the evolution of consciousness and the operations of all mental processes. Types of emotion relate differentially to types or levels of consciousness. Unbridled imagination and the ability for sympathetic regulation of empathy may represent both potential gains and losses from the evolution and ontogeny of emotion processes and consciousness. Unresolved issues include psychology's neglect of levels of consciousness that are distinct from access or reflective consciousness and use of the term “unconscious mind” as a dumpster for all mental processes that are considered unreportable. The relation of memes and the mirror neur...
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paper_title: Human and computer recognition of facial expressions of emotion

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Neuropsychological and neuroimaging evidence suggests that the human brain contains facial expression recognition detectors specialized for specific discrete emotions. However, some human behavioral data suggest that humans recognize expressions as similar and not discrete entities. This latter observation has been taken to indicate that internal representations of facial expressions may be best characterized as varying along continuous underlying dimensions. To examine the potential compatibility of these two views, the present study compared human and support vector machine (SVM) facial expression recognition performance. Separate SVMs were trained to develop fully automatic optimal recognition of one of six basic emotional expressions in real-time with no explicit training on expression similarity. Performance revealed high recognition accuracy for expression prototypes. Without explicit training of similarity detection, magnitude of activation across each emotion-specific SVM captured human judgments of expression similarity. This evidence suggests that combinations of expert classifiers from separate internal neural representations result in similarity judgments between expressions, supporting the appearance of a continuous underlying dimensionality. Further, these data suggest similarity in expression meaning is supported by superficial similarities in expression appearance.
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paper_title: Deciphering the face

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We argue that to make robust computer vision algorithms for face analysis and recognition, these should be based on configural and shape features. In this model, the most important task to be solved by computer vision researchers is that of accurate detection of facial features, rather than recognition. We base our arguments on recent results in cognitive science and neuroscience. In particular, we show that different facial expressions of emotion have diverse uses in human behavior/cognition and that a facial expression may be associated to multiple emotional categories. These two results are in contradiction with the continuous models in cognitive science, the limbic assumption in neuroscience and the multidimensional approaches typically employed in computer vision. Thus, we propose an alternative hybrid continuous-categorical approach to the perception of facial expressions and show that configural and shape features are most important for the recognition of emotional constructs by humans. We illustrate how these image cues can be successfully exploited by computer vision algorithms. Throughout the paper, we discuss the implications of these results in applications in face recognition and human-computer interaction.
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paper_title: A computational shape-based model of anger and sadness justifies a configural representation of faces

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Abstract Research suggests that configural cues (second-order relations) play a major role in the representation and classification of face images; making faces a “special” class of objects, since object recognition seems to use different encoding mechanisms. It is less clear, however, how this representation emerges and whether this representation is also used in the recognition of facial expressions of emotion. In this paper, we show how configural cues emerge naturally from a classical analysis of shape in the recognition of anger and sadness. In particular our results suggest that at least two of the dimensions of the computational (cognitive) space of facial expressions of emotion correspond to pure configural changes. The first of these dimensions measures the distance between the eyebrows and the mouth, while the second is concerned with the height–width ratio of the face. Under this proposed model, becoming a face “expert” would mean to move from the generic shape representation to that based on configural cues. These results suggest that the recognition of facial expressions of emotion shares this expertise property with the other processes of face processing.
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paper_title: Probabilistic Non-linear Principal Component Analysis with Gaussian Process Latent Variable Models

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Summarising a high dimensional data set with a low dimensional embedding is a standard approach for exploring its structure. In this paper we provide an overview of some existing techniques for discovering such embeddings. We then introduce a novel probabilistic interpretation of principal component analysis (PCA) that we term dual probabilistic PCA (DPPCA). The DPPCA model has the additional advantage that the linear mappings from the embedded space can easily be non-linearised through Gaussian processes. We refer to this model as a Gaussian process latent variable model (GP-LVM). Through analysis of the GP-LVM objective function, we relate the model to popular spectral techniques such as kernel PCA and multidimensional scaling. We then review a practical algorithm for GP-LVMs in the context of large data sets and develop it to also handle discrete valued data and missing attributes. We demonstrate the model on a range of real-world and artificially generated data sets.
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paper_title: Bayes Optimality in Linear Discriminant Analysis

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We present an algorithm which provides the one-dimensional subspace where the Bayes error is minimized for the C class problem with homoscedastic Gaussian distributions. Our main result shows that the set of possible one-dimensional spaces v, for which the order of the projected class means is identical, defines a convex region with associated convex Bayes error function g(v). This allows for the minimization of the error function using standard convex optimization algorithms. Our algorithm is then extended to the minimization of the Bayes error in the more general case of heteroscedastic distributions. This is done by means of an appropriate kernel mapping function. This result is further extended to obtain the d-dimensional solution for any given d, by iteratively applying our algorithm to the null space of the (d - 1)-dimensional solution. We also show how this result can be used to improve up on the outcomes provided by existing algorithms, and derive a low-computational cost, linear approximation. Extensive experimental validations are provided to demonstrate the use of these algorithms in classification, data analysis and visualization.
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paper_title: Early Processing Of Visual Information

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An introduction is given to a theory of early visual information processing. The theory has been implemented, and examples are given of images at various stages of analysis. It is argued that the first step of consequence is to compute a primitive but rich description of the grey-level changes present in an image. The description is expressed in a vocabulary of kinds of intensity change (EDGE, SHADING-EDGE, EXTENDED-EDGE, LINE, BLOB etc.). Modifying parameters are bound to the elements in the description, specifying their POSITION, ORIENTATION, TERMINATION points, CONTRAST, SIZE and FUZZINESS. This description is obtained from the intensity array by fixed techniques, and it is called the primal sketch. For most images, the primal sketch is large and unwieldy. The second important step in visual information processing is to group its contents in a way that is appropriate for later recognition. From our ability to interpret drawings with little semantic content, one may infer the presence in our perceptual equipment of symbolic processes that can define 'placetokens' in an image in various ways, and can group them according to certain rules. Homomorphic techniques fail to account for many of these grouping phenomena, whose explanations require mechanisms of construction rather than mechanisms of detection. The necessary grouping of elements in the primal sketch may be achieved by a mechanism that has available the processes inferred from above, together with the ability to select items by first order discriminations acting on the elements' parameters. Only occasionally do these mechanisms use downward-flowing information about the contents of the particular image being processed. It is argued that 'non-attentive' vision is in practice implemented by these grouping operations and first order discriminations acting on the primal sketch. The class of computations so obtained differs slightly from the class of second order operations on the intensity array. The extraction of a form from the primal sketch using these techniques amounts to the separation of figure from ground. It is concluded that most of the separation can be carried out by using techniques that do not depend upon the particular image in question. Therefore, figure-ground separation can normally precede the description of the shape of the extracted form. Up to this point, higher-level knowledge and purpose are brought to bear on only a few of the decisions taken during the processing. This relegates the widespread use of downward-flowing information to a later stage than is found in current machine-vision programs, and implies that such knowledge should influence the control of, rather than interfering with, the actual data-processing that is taking place lower down.
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paper_title: The Effect of Feature Displacement on the Perception of Well-Known Faces

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The effect of feature displacement within two well-known faces (Terry Wogan and Cyril Smith) was examined. Image processing equipment was used to produce stimuli in which the features of an original facial image were displaced to form a number of modified images. This technique was first reported by Haig, in a recognition study in which the effect of feature displacement within unfamiliar faces was investigated. In the present experiment a perceptual judgement task was carried out in which subjects were presented with a number of modified faces and asked to judge how dissimilar these were with respect to an original image. A multidimensional scaling analysis of the comparative judgements of the subjects revealed a two-dimensional solution involving displacement of the eyes and mouth. A clear division between up/down and inward/outward displacement within these features (particularly the eyes) was observed. A similar pattern of results was found for both well-known faces. This result indicates that subject...
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paper_title: Recognizing Imprecisely Localized, Partially Occluded, and Expression Variant Faces from a Single Sample per Class

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The classical way of attempting to solve the face (or object) recognition problem is by using large and representative data sets. In many applications, though, only one sample per class is available to the system. In this contribution, we describe a probabilistic approach that is able to compensate for imprecisely localized, partially occluded, and expression-variant faces even when only one single training sample per class is available to the system. To solve the localization problem, we find the subspace (within the feature space, e.g., eigenspace) that represents this error for each of the training images. To resolve the occlusion problem, each face is divided into k local regions which are analyzed in isolation. In contrast with other approaches where a simple voting space is used, we present a probabilistic method that analyzes how "good" a local match is. To make the recognition system less sensitive to the differences between the facial expression displayed on the training and the testing images, we weight the results obtained on each local area on the basis of how much of this local area is affected by the expression displayed on the current test image.
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paper_title: Three-Dimensional Object Recognition from Single Two-Dimensional Images

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Abstract A computer vision system has been implemented that can recognize three-dimensional objects from unknown viewpoints in single gray-scale images. Unlike most other approaches, the recognition is accomplished without any attempt to reconstruct depth information bottom-up from the visual input. Instead, three other mechanisms are used that can bridge the gap between the two-dimensional image and knowledge of three-dimensional objects. First, a process of perceptual organization is used to form groupings and structures in the image that are likely to be invariant over a wide range of viewpoints. Second, a probabilistic ranking method is used to reduce the size of the search space during model-based matching. Finally, a process of spatial correspondence brings the projections of three-dimensional models into direct correspondence with the image by solving for unknown viewpoint and model parameters. A high level of robustness in the presence of occlusion and missing data can be achieved through full application of a viewpoint consistency constraint. It is argued that similar mechanisms and constraints form the basis for recognition in human vision.
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paper_title: Emotion Perception in Emotionless Face Images Suggests a Norm-based Representation

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Perception of facial expressions of emotion is generally assumed to correspond to underlying muscle movement. However, it is often observed that some individuals have sadder or angrier faces, even for neutral, motionless faces. Here, we report on one such effect caused by simple static configural changes. In particular, we show four variations in the relative vertical position of the nose, mouth, eyes, and eyebrows that affect the perception of emotion in neutral faces. The first two configurations make the vertical distance between the eyes and mouth shorter than average, resulting in the perception of an angrier face. The other two configurations make this distance larger than average, resulting in the perception of sadness. These perceptions increase with the amount of configural change, suggesting a representation based on variations from a norm (prototypical) face. Understanding emotion is a central problem of cognitive science and is fundamental to the study of human behavior (Darwin, 1872; Russell, 2003). To properly study this, it is essential that we identify how emotions are represented in the brain – their psychological space. Here, we are concerned with the underlying dimensions of the face space used to represent and recognize sadness and anger from pictures. Ekman and colleagues (Ekman & Friesen, 1978; Ekman & Oster, 1979) following in the footsteps of Darwin (1872), identified six universal emotional expressions (anger, sadness, fear, surprise, happiness, and disgust) assumed to have some role within the theory of evolution (Schmidt & Cohn, 2001). According to this hypothesis, all people should be equipped with the capacity to express and perceive these emotions and their associated facial expressions. Ekman and Friesen’s (1978) fundamental work demonstrated how each of these emotional expressions is created by moving particular facial muscles to certain positions, and several authors have shown their universality (Ekman & Oster, 1979; Izard, 1994). Under this view, motion primitives constitute the dimensions of the underlying computational face space. However, everyday experience suggests that other elements may be employed to classify faces into one group or another (Zebrowitz, 1997). For example, Montepare and Dobish (2003) show that there is an agreement among subjects judging the perception of emotions and social cues To appear in Journal of Vision. from faces with no particular emotional expression. Zebrowitz et al. (2007) found that impressions of emotion expression on faces were partially mediated by babyfacedness. And, Lyons et al. (2000) found that an upward tilt of the head produces a two-dimensional retinal image with facial features associated with negative affect. Similarly, a downward tilt produces a two-dimensional image of positive affect. One possibility is that neutral faces resembling configurations of a given expression elicit perception of such emotion (Zebrowitz, 1997; Montepare & Dobish, 2003; Zebrowitz et al., 2007). This effect is known as the emotion overgeneralization hypothesis. Under this view, the major question remains as to which facial features are used to represent emotions and thus which ones are associated with these overgeneralizations. This is important, because these features are the ones used to code the psychological (face) space. Using Ekman and Friesen’s (1978) collection of face images, where subjects were instructed to move their face muscles to display each of the six universal emotions, we observed that the distance between the inner corner of the two eyebrows and the mouth increases when one expresses sadness (from an average of 233.12 pixels in neutral expressions to 248.8 for sadness). The same distance decreases to 220.12 pixels when one expresses anger. Hence, one hypothesis is that individuals with an “elongated” face (i.e., a larger vertical distance between internal features) will be perceived sadder than average, while people with a reduced vertical distance between facial features will be perceived as angrier. In the present paper, we show results in favor of this hypothesis. In Experiment 1, we show that the vertical distance between eyes, nose and mouth of an individual face is correlated with the perception of anger and sadness in face images displaying a neutral expression. When the vertical distance between eyes and mouth is made larger than that of the average face (i.e., the average distance within a population), the perception of sadness increases. A decrease of the same distance from that of the average face increases the perception of sadness. This result suggests that configural (second-order) cues are used by the cognitive system to represent and recognize anger and sadness. Our results further demonstrate that the perception of anger and sadness increases linearly with the eye-mouth distance, suggesting the existence of a norm-based face space where each face is represented as a variation of a norm (sometimes called mean or prototype) face (Valentine, 1991). We also show that this effect is asymmetric, because the linear increase only occurs when the second image has a large intra-feature distance than the first one. When the larger eye-to-mouth distance is on the first image, this effect is diminished. This is because when the second image is closer to the mean face, the density of the face space increases, making it more difficult to distinguish between categories. If the face space used to represent facial expressions of emotion is indeed norm-based, then those images with a shorter than average eye-mouth distance should not be perceived as sadder, while those with a larger than average distance should not be seen as angrier. That is to say, the To appear in Journal of Vision. perception of sadness and anger should not traverse to the other side of the “mean” face, since these correspond to two distinct psychological categories. This is the same effect seen in face recognition, where moving along a single dimension of the face space results in the observation of two distinct identities after crossing over to opposite sides of the mean face. This effect is verified in Experiment 2. In this second experiment, subjects were shown the images with shorter and larger eye-to-mouth distance simultaneously. Their task was now to determine whether the second image appeared more angry or sad than the first. Subjects consistently classified the images with larger vertical distance between internal facial features as sadder and image with a shorter distance as angrier. The results reported in this paper have implications in the interpretation and design of psychophysical experiments, social behavior and political studies as well as in defining computational models and developing human-computer interaction systems. Furthermore, our results are important for the analysis of art, where artist may have used such configurations to emphasize expressions --as it seems to be the case in Grant Wood’s “American Gothic” portrait. The perceptual biases described here are also relevant to interpret some social interactions (Hassin & Trope, 2000), and seem to affect the perception of attractiveness (Paunonen, 1999; Thornhill & Gangestad, 1999) and identity (Martinez, 2003; Ackerman et al., 2006). These results also call for additional research to help determine to what extent configural, shape and motion cues are combined with one another to influence the perception of facial expressions of emotion.
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paper_title: Rotation Invariant Kernels and Their Application to Shape Analysis

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Shape analysis requires invariance under translation, scale, and rotation. Translation and scale invariance can be realized by normalizing shape vectors with respect to their mean and norm. This maps the shape feature vectors onto the surface of a hypersphere. After normalization, the shape vectors can be made rotational invariant by modeling the resulting data using complex scalar-rotation invariant distributions defined on the complex hypersphere, e.g., using the complex Bingham distribution. However, the use of these distributions is hampered by the difficulty in estimating their parameters and the nonlinear nature of their formulation. In the present paper, we show how a set of kernel functions that we refer to as rotation invariant kernels can be used to convert the original nonlinear problem into a linear one. As their name implies, these kernels are defined to provide the much needed rotation invariance property allowing one to bypass the difficulty of working with complex spherical distributions. The resulting approach provides an easy, fast mechanism for 2D & 3D shape analysis. Extensive validation using a variety of shape modeling and classification problems demonstrates the accuracy of this proposed approach.
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paper_title: Statistical learning theory

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A comprehensive look at learning and generalization theory. The statistical theory of learning and generalization concerns the problem of choosing desired functions on the basis of empirical data. Highly applicable to a variety of computer science and robotics fields, this book offers lucid coverage of the theory as a whole. Presenting a method for determining the necessary and sufficient conditions for consistency of learning process, the author covers function estimates from small data pools, applying these estimations to real-life problems, and much more.
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paper_title: A computational shape-based model of anger and sadness justifies a configural representation of faces

paper_content:

Abstract Research suggests that configural cues (second-order relations) play a major role in the representation and classification of face images; making faces a “special” class of objects, since object recognition seems to use different encoding mechanisms. It is less clear, however, how this representation emerges and whether this representation is also used in the recognition of facial expressions of emotion. In this paper, we show how configural cues emerge naturally from a classical analysis of shape in the recognition of anger and sadness. In particular our results suggest that at least two of the dimensions of the computational (cognitive) space of facial expressions of emotion correspond to pure configural changes. The first of these dimensions measures the distance between the eyebrows and the mouth, while the second is concerned with the height–width ratio of the face. Under this proposed model, becoming a face “expert” would mean to move from the generic shape representation to that based on configural cues. These results suggest that the recognition of facial expressions of emotion shares this expertise property with the other processes of face processing.
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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: Low-dimensional procedure for the characterization of human faces

paper_content:

A method is presented for the representation of (pictures of) faces. Within a specified framework the representation is ideal. This results in the characterization of a face, to within an error bound, by a relatively low-dimensional vector. The method is illustrated in detail by the use of an ensemble of pictures taken for this purpose.
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paper_title: Inversion and Configuration of Faces

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If the mouth and eyes of a face are inverted, the altered construction appears grotesque when upright, but not when upside-down. Three studies of this "Thatcher illusion" employed faces that were grotesque when upright because: (a) their eyes and mouths had been inverted ("Thatcherized" faces), (b) their eyes and mouths had been moved (spatially distorted faces), or (c) they had grotesque posed expressions. Inversion reduced the apparent grotesqueness of both Thatcherized and spatially distorted faces, but not grotesque-expression faces. Moreover, Thatcherized and distorted faces, although not grotesque-expression faces, were judged as more similar to normal, smiling faces when face-pairs were inverted than when they were upright. Similarity ratings to inverted face-pairs were correlated with latencies of response to these pairs in a task that encouraged attention to components (e.g., mouths, eyes) rather than wholistic properties. Similarity ratings to upright face-pairs showed no such correlation, and this and other findings suggested that although similarity ratings to upright faces are based on wholistic information, similarity ratings to inverted faces are based largely on components. The Thatcher illusion reflects a disruption of encoding of wholistic information when faces are inverted.
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paper_title: Configurational Information in Face Perception

paper_content:

A new facial composites technique is demonstrated, in which photographs of the top and bottom halves of different familiar faces fuse to form unfamiliar faces when aligned with each other. The perception of a novel configuration in such composite stimuli is sufficiently convincing to interfere with identification of the constituent parts (experiment 1), but this effect disappears when stimuli are inverted (experiment 2). Difficulty in identifying the parts of upright composites is found even for stimuli made from parts of unfamiliar faces that have only ever been encountered as face fragments (experiment 3). An equivalent effect is found for composites made from internal and external facial features of well-known people (experiment 4). These findings demonstrate the importance of configurational information in face perception, and that configurations are only properly perceived in upright faces.
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paper_title: Eigenfaces for Recognition

paper_content:

We have developed a near-real-time computer system that can locate and track a subject's head, and then recognize the person by comparing characteristics of the face to those of known individuals. The computational approach taken in this system is motivated by both physiology and information theory, as well as by the practical requirements of near-real-time performance and accuracy. Our approach treats the face recognition problem as an intrinsically two-dimensional (2-D) recognition problem rather than requiring recovery of three-dimensional geometry, taking advantage of the fact that faces are normally upright and thus may be described by a small set of 2-D characteristic views. The system functions by projecting face images onto a feature space that spans the significant variations among known face images. The significant features are known as "eigenfaces," because they are the eigenvectors (principal components) of the set of faces; they do not necessarily correspond to features such as eyes, ears, and noses. The projection operation characterizes an individual face by a weighted sum of the eigenface features, and so to recognize a particular face it is necessary only to compare these weights to those of known individuals. Some particular advantages of our approach are that it provides for the ability to learn and later recognize new faces in an unsupervised manner, and that it is easy to implement using a neural network architecture.
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paper_title: Face recognition: features versus templates

paper_content:

Two new algorithms for computer recognition of human faces, one based on the computation of a set of geometrical features, such as nose width and length, mouth position, and chin shape, and the second based on almost-gray-level template matching, are presented. The results obtained for the testing sets show about 90% correct recognition using geometrical features and perfect recognition using template matching. >
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paper_title: Detecting Faces in Images : A Survey

paper_content:

Images containing faces are essential to intelligent vision-based human-computer interaction, and research efforts in face processing include face recognition, face tracking, pose estimation and expression recognition. However, many reported methods assume that the faces in an image or an image sequence have been identified and localized. To build fully automated systems that analyze the information contained in face images, robust and efficient face detection algorithms are required. Given a single image, the goal of face detection is to identify all image regions which contain a face, regardless of its 3D position, orientation and lighting conditions. Such a problem is challenging because faces are non-rigid and have a high degree of variability in size, shape, color and texture. Numerous techniques have been developed to detect faces in a single image, and the purpose of this paper is to categorize and evaluate these algorithms. We also discuss relevant issues such as data collection, evaluation metrics and benchmarking. After analyzing these algorithms and identifying their limitations, we conclude with several promising directions for future research.
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paper_title: Active appearance models

paper_content:

We describe a new method of matching statistical models of appearance to images. A set of model parameters control modes of shape and gray-level variation learned from a training set. We construct an efficient iterative matching algorithm by learning the relationship between perturbations in the model parameters and the induced image errors.
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paper_title: Recognizing Imprecisely Localized, Partially Occluded, and Expression Variant Faces from a Single Sample per Class

paper_content:

The classical way of attempting to solve the face (or object) recognition problem is by using large and representative data sets. In many applications, though, only one sample per class is available to the system. In this contribution, we describe a probabilistic approach that is able to compensate for imprecisely localized, partially occluded, and expression-variant faces even when only one single training sample per class is available to the system. To solve the localization problem, we find the subspace (within the feature space, e.g., eigenspace) that represents this error for each of the training images. To resolve the occlusion problem, each face is divided into k local regions which are analyzed in isolation. In contrast with other approaches where a simple voting space is used, we present a probabilistic method that analyzes how "good" a local match is. To make the recognition system less sensitive to the differences between the facial expression displayed on the training and the testing images, we weight the results obtained on each local area on the basis of how much of this local area is affected by the expression displayed on the current test image.
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paper_title: Rotation Invariant Kernels and Their Application to Shape Analysis

paper_content:

Shape analysis requires invariance under translation, scale, and rotation. Translation and scale invariance can be realized by normalizing shape vectors with respect to their mean and norm. This maps the shape feature vectors onto the surface of a hypersphere. After normalization, the shape vectors can be made rotational invariant by modeling the resulting data using complex scalar-rotation invariant distributions defined on the complex hypersphere, e.g., using the complex Bingham distribution. However, the use of these distributions is hampered by the difficulty in estimating their parameters and the nonlinear nature of their formulation. In the present paper, we show how a set of kernel functions that we refer to as rotation invariant kernels can be used to convert the original nonlinear problem into a linear one. As their name implies, these kernels are defined to provide the much needed rotation invariance property allowing one to bypass the difficulty of working with complex spherical distributions. The resulting approach provides an easy, fast mechanism for 2D & 3D shape analysis. Extensive validation using a variety of shape modeling and classification problems demonstrates the accuracy of this proposed approach.
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paper_title: Active Appearance Models with Rotation Invariant Kernels

paper_content:

2D Active Appearance Models (AAM) and 3D Morphable Models (3DMM) are widely used techniques. AAM provide a fast fitting process, but may represent unwanted 3D transformations unless strictly constrained not to do so. The reverse is true for 3DMM. The two approaches also require of a pre-alignment of their 2D or 3D shapes before the modeling can be carried out which may lead to errors. Furthermore, current models are insufficient to represent nonlinear shape and texture variations. In this paper, we derive a new approach that can model nonlinear changes in examples without the need of a pre-alignment step. In addition, we show how the proposed approach carries the above mentioned advantages of AAM and 3DMM. To achieve this goal, we take advantage of the inherent properties of complex spherical distributions, which provide invariance to translation, scale and rotation. To reduce the complexity of parameter estimation we take advantage of a recent result that shows how to estimate spherical distributions using their Euclidean counterpart, e.g., the Gaussians. This leads to the definition of Rotation Invariant Kernels (RIK) for modeling nonlinear shape changes. We show the superiority of our algorithm to AAM in several face datasets. We also show how the derived algorithm can be used to model complex 3D facial expression changes observed in American Sign Language (ASL).
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paper_title: Features versus Context: An Approach for Precise and Detailed Detection and Delineation of Faces and Facial Features

paper_content:

The appearance-based approach to face detection has seen great advances in the last several years. In this approach, we learn the image statistics describing the texture pattern (appearance) of the object class we want to detect, e.g., the face. However, this approach has had limited success in providing an accurate and detailed description of the internal facial features, i.e., eyes, brows, nose, and mouth. In general, this is due to the limited information carried by the learned statistical model. While the face template is relatively rich in texture, facial features (e.g., eyes, nose, and mouth) do not carry enough discriminative information to tell them apart from all possible background images. We resolve this problem by adding the context information of each facial feature in the design of the statistical model. In the proposed approach, the context information defines the image statistics most correlated with the surroundings of each facial component. This means that when we search for a face or facial feature, we look for those locations which most resemble the feature yet are most dissimilar to its context. This dissimilarity with the context features forces the detector to gravitate toward an accurate estimate of the position of the facial feature. Learning to discriminate between feature and context templates is difficult, however, because the context and the texture of the facial features vary widely under changing expression, pose, and illumination, and may even resemble one another. We address this problem with the use of subclass divisions. We derive two algorithms to automatically divide the training samples of each facial feature into a set of subclasses, each representing a distinct construction of the same facial component (e.g., closed versus open eyes) or its context (e.g., different hairstyles). The first algorithm is based on a discriminant analysis formulation. The second algorithm is an extension of the AdaBoost approach. We provide extensive experimental results using still images and video sequences for a total of 3,930 images. We show that the results are almost as good as those obtained with manual detection.
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paper_title: The resolution of facial expressions of emotion

paper_content:

Much is known on how facial expressions of emotion are produced, including which individual muscles are most active in each expression. Yet, little is known on how this information is interpreted by the human visual system. This paper presents a systematic study of the image dimensionality of facial expressions of emotion. In particular, we investigate how recognition degrades when the resolution of the image (i.e., number of pixels when seen as a 5.3 by 8 degrees stimulus) is reduced. We show that recognition is only impaired in practise when the image resolution goes below 20×30 pixels. A study of the confusion tables demonstrates that each expression of emotion is consistently confused by a small set of alternatives and that the confusion is not symmetric, i.e., misclassifying emotion a as b does not imply we will mistake b for a. This asymmetric pattern is consistent over the different image resolutions and cannot be explained by the similarity of muscle activation. Furthermore, although women are generally better at recognizing expressions of emotion at all resolutions, the asymmetry patterns are the same. We discuss the implications of these results for current models of
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paper_title: Computing Smooth Time Trajectories for Camera and Deformable Shape in Structure from Motion with Occlusion

paper_content:

We address the classical computer vision problems of rigid and nonrigid structure from motion (SFM) with occlusion. We assume that the columns of the input observation matrix W describe smooth 2D point trajectories over time. We then derive a family of efficient methods that estimate the column space of W using compact parameterizations in the Discrete Cosine Transform (DCT) domain. Our methods tolerate high percentages of missing data and incorporate new models for the smooth time trajectories of 2D-points, affine and weak-perspective cameras, and 3D deformable shape. We solve a rigid SFM problem by estimating the smooth time trajectory of a single camera moving around the structure of interest. By considering a weak-perspective camera model from the outset, we directly compute euclidean 3D shape reconstructions without requiring postprocessing steps such as euclidean upgrade and bundle adjustment. Our results on real SFM data sets with high percentages of missing data compared positively to those in the literature. In nonrigid SFM, we propose a novel 3D shape trajectory approach that solves for the deformable structure as the smooth time trajectory of a single point in a linear shape space. A key result shows that, compared to state-of-the-art algorithms, our nonrigid SFM method can better model complex articulated deformation with higher frequency DCT components while still maintaining the low-rank factorization constraint. Finally, we also offer an approach for nonrigid SFM when W is presented with missing data.
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paper_title: Kernel non-rigid structure from motion

paper_content:

Non-rigid structure from motion (NRSFM) is a difficult, underconstrained problem in computer vision. The standard approach in NRSFM constrains 3D shape deformation using a linear combination of K basis shapes; the solution is then obtained as the low-rank factorization of an input observation matrix. An important but overlooked problem with this approach is that non-linear deformations are often observed; these deformations lead to a weakened low-rank constraint due to the need to use additional basis shapes to linearly model points that move along curves. Here, we demonstrate how the kernel trick can be applied in standard NRSFM. As a result, we model complex, deformable 3D shapes as the outputs of a non-linear mapping whose inputs are points within a low-dimensional shape space. This approach is flexible and can use different kernels to build different non-linear models. Using the kernel trick, our model complements the low-rank constraint by capturing non-linear relationships in the shape coefficients of the linear model. The net effect can be seen as using non-linear dimensionality reduction to further compress the (shape) space of possible solutions.
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paper_title: What the face reveals : basic and applied studies of spontaneous expression using the facial action coding system (FACS)

paper_content:

Foreword Introduction The study of spontaneous facial expression in psychology I: BASIC RESEARCH ON EMOTION 1. Is the startle reaction an emotion? 2. The asymmetry of facial actions is inconsisten with models of hemispheric specialization 3. Coherence between expressive and experiential systmes in emotion 4. Will the real relationship between facial expression and affective experience please stand up: The case of exhilaration 5. Extraversion, alcohol, and enjoyment 6. Signs of appeasement: Evidence for the distinct displays of embarrassment, amusement, and shame 7. Genuine, suppressed, and faked facial behavior during exacerbation of chronic low back pain 8. The consistency of facial expressions of pain: a comparison across modalities 9. Smiles when lying 10. Behavioral markers and recognizability of the smile of enjoyment 11. Components and recognition of facial expression in the communication of emotion by actors 12. Differentiating emotion elicited and deliberate emotional facial expressions 13. Japanese and American infants' responses to arm restraint 14. Differential facial responses to four basic tests in newborns II: APPLIED RESEARCH 15. Facial expressions in affective disorders 16. Emotional experience and epxression in schizophrenia and depression 17. Interaction regulations used by schizophrenic and psychosomatic patients: Studies on facial behavior in dyadic interactions 18. Nonverbal expression of psychological states in psychiatric patients 19. Depression and suicide faces 20. Prototypical affective microsequences in psychotherapeutic interaction 21. Facial expressions of emotion and psychopathology in adolescent boys 22. Type A behavior pattern: Facial behavior and speech components Conclusion What we have learned by measuring the face Index
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paper_title: Emotion circuits in the brain.

paper_content:

The field of neuroscience has, after a long period of looking the other way, again embraced emotion as an important research area. Much of the progress has come from studies of fear, and especially fear conditioning. This work has pin- pointed the amygdala as an important component of the system involved in the acqui- sition, storage, and expression of fear memory and has elucidated in detail how stimuli enter, travel through, and exit the amygdala. Some progress has also been made in understanding the cellular and molecular mechanisms that underlie fear conditioning, and recent studies have also shown that the findings from experimental animals apply to the human brain. It is important to remember why this work on emotion succeeded where past efforts failed. It focused on a psychologically well-defined aspect of emo- tion, avoided vague and poorly defined concepts such as "affect," "hedonic tone," or "emotional feelings," and used a simple and straightforward experimental approach. With so much research being done in this area today, it is important that the mistakes of the past not be made again. It is also time to expand from this foundation into broader aspects of mind and behavior
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paper_title: Smile Through Your Fear and Sadness Transmitting and Identifying Facial Expression Signals Over a Range of Viewing Distances

paper_content:

It is well established that animal communication signals have adapted to the evolutionary pressures of their environment. For example, the low-frequency vocalizations of the elephant are tailored to long-range communications, whereas the high-frequency trills of birds are adapted to their more localized acoustic niche. Like the voice, the human face transmits social signals about the internal emotional state of the transmitter. Here, we address two main issues: First, we characterized the spectral composition of the facial features signaling each of the six universal expressions of emotion (happiness, sadness, fear, disgust, anger, and surprise). From these analyses, we then predicted and tested the effectiveness of the transmission of emotion signals over different viewing distances. We reveal a gradient of recognition over viewing distances constraining the relative adaptive usefulness of facial expressions of emotion (distal expressions are good signals over a wide range of viewing distances; proximal ...
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paper_title: Expressing fear enhances sensory acquisition

paper_content:

This behavioral study finds that when subjects pose expressions of fear, they have a subjectively larger visual field, faster eye movements, and an increase in nasal volume and air velocity during breathing in. Posing expressions of disgust, an expression objectively opposite to fear, produced opposite results. Emotional facial expressions may therefore modify preparedness for perception and action.
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paper_title: Impaired Face Processing in Autism: Fact or Artifact?

paper_content:

Within the last 10 years, there has been an upsurge of interest in face processing abilities in autism which has generated a proliferation of new empirical demonstrations employing a variety of measuring techniques. Observably atypical social behaviors early in the development of children with autism have led to the contention that autism is a condition where the processing of social information, particularly faces, is impaired. While several empirical sources of evidence lend support to this hypothesis, others suggest that there are conditions under which autistic individuals do not differ from typically developing persons. The present paper reviews this bulk of empirical evidence, and concludes that the versatility and abilities of face processing in persons with autism have been underestimated.
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paper_title: The resolution of facial expressions of emotion

paper_content:

Much is known on how facial expressions of emotion are produced, including which individual muscles are most active in each expression. Yet, little is known on how this information is interpreted by the human visual system. This paper presents a systematic study of the image dimensionality of facial expressions of emotion. In particular, we investigate how recognition degrades when the resolution of the image (i.e., number of pixels when seen as a 5.3 by 8 degrees stimulus) is reduced. We show that recognition is only impaired in practise when the image resolution goes below 20×30 pixels. A study of the confusion tables demonstrates that each expression of emotion is consistently confused by a small set of alternatives and that the confusion is not symmetric, i.e., misclassifying emotion a as b does not imply we will mistake b for a. This asymmetric pattern is consistent over the different image resolutions and cannot be explained by the similarity of muscle activation. Furthermore, although women are generally better at recognizing expressions of emotion at all resolutions, the asymmetry patterns are the same. We discuss the implications of these results for current models of
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paper_title: Understanding emotions from standardized facial expressions in autism and normal development

paper_content:

The study investigated the recognition of standardized facial expressions of emotion (anger, fear, disgust, happiness, sadness, surprise) at a perceptual level (experiment 1) and at a semantic level (experiments 2 and 3) in children with autism (N= 20) and normally developing children (N= 20). Results revealed that children with autism were as able as controls to recognize all six emotions with different intensity levels, and that they made the same type of errors. These negative findings are discussed in relation to (1) previous data showing specific impairment in autism in recognizing the belief-based expression of surprise, (2) previous data showing specific impairment in autism in recognizing fear, and (3) the convergence of findings that individuals with autism, like patients with amygdala damage, pass a basic emotions recognition test but fail to recognize more complex stimuli involving the perception of faces or part of faces.
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Title: A Model of the Perception of Facial Expressions of Emotion by Humans: Research Overview and Perspectives

Section 1: Introduction
Description 1: Introduce the significance of facial expressions in human cognition and communication, and the importance of computational models in understanding and emulating this perception.

Section 2: Facial Expressions: From Production to Perception
Description 2: Discuss the production of facial expressions, their classification (categorical vs. continuous models), and the universality and recognition of different emotions.

Section 3: A Model of the Perception of Facial Expressions of Emotion
Description 3: Outline the proposed cognitive and computational model, explaining how it integrates both categorical and continuous representation of emotions.

Section 4: Dimensions of the Model
Description 4: Describe the various dimensions and features of the model, particularly configural and shape features essential for recognizing facial expressions of emotion.

Section 5: Precise Detection of Faces and Facial Features
Description 5: Address the challenges in accurately detecting facial features and present approaches to improve detection, emphasizing the need for precise localization of facial landmarks.

Section 6: Discussion
Description 6: Provide an analysis of the findings, examine the implications for human cognition and machine learning, and suggest areas for further research to improve facial expression recognition systems.

Section 7: Conclusions
Description 7: Summarize the study's contributions to understanding human perception of facial expressions and outline future directions for advancing face recognition technology.