Human Gender Classification: A Review
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paper_title: A survey on vision-based human action recognition

paper_content:

Vision-based human action recognition is the process of labeling image sequences with action labels. Robust solutions to this problem have applications in domains such as visual surveillance, video retrieval and human-computer interaction. The task is challenging due to variations in motion performance, recording settings and inter-personal differences. In this survey, we explicitly address these challenges. We provide a detailed overview of current advances in the field. Image representations and the subsequent classification process are discussed separately to focus on the novelties of recent research. Moreover, we discuss limitations of the state of the art and outline promising directions of research.
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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: A survey of biometric recognition methods

paper_content:

Biometric recognition refers to an automatic recognition of individuals based on a feature vector (s) derived from their physiological and/or behavioral characteristic. Biometric recognition systems should provide a reliable personal recognition schemes to either confirm or determine the identity of an individual. Applications of such a system include computer systems security, secure electronic banking, mobile phones, credit cards, secure access to buildings, health and social services. By using biometrics a person could be identified based on "who she/he is" rather then "what she/he has" (card, token, key) or "what she/he knows" (password, PIN). In this paper, a brief overview of biometric methods, both unimodal and multimodal, and their advantages and disadvantages, will be presented.
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paper_title: The nature of gender

paper_content:

I explain a biosocial model of women’s gendered behavior (behavior on which the sexes differ). This model integrates a macro sociological theory with a biological theory derived from primate behavior. The sociological model is designed to explain changes in the relationship between sex and behavior over time or between groups. The biological model is designed to explain individual within-sex variance and between-sex variance in gendered behavior in a cohort. Results from an original study are presented to demonstrate that within-sex variance in women’s gendered behavior is explained well by the primate model. I conclude that human nature is gendered. The implications of this conclusion are explored for demographic and other social science research.
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paper_title: An experimental comparison of gender classification methods

paper_content:

Successful face analysis requires robust methods. It has been hard to compare the methods due to different experimental setups. We carried out a comparison study for the state-of-the-art gender classification methods to find out their actual reliability. The main contributions are comprehensive and comparable classification results for the gender classification methods combined with automatic real-time face detection and, in addition, with manual face normalization. We also experimented by combining gender classifier outputs arithmetically. This lead to increased classification accuracies. Furthermore, we contribute guidelines to carry out classification experiments, knowledge on the strengths and weaknesses of the gender classification methods, and two new variants of the known methods.
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paper_title: Gender HCI: Results To Date Regarding Issues in Problem-Solving Software

paper_content:

We are investigating gender HCI issues in problemsolving software, using quantitative and qualitative empirical methods. Our research approach uses formative work to understand the existence and attributes of gender-relevant design features, based on which we then design and implement new features in our research prototype. Finally, we perform summative empirical work to evaluate effectiveness. AUTHOR KEYWORDS Gender HCI, end-user software engineering
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paper_title: A Comparative Analysis of Gender Classification Techniques

paper_content:

Over the period of time, automated classification of gender has gained enormous significance and has become an active area of research. Many researchers have put a lot of effort and have produced quality research in this area. Still, there is an immense potential in this field because of its utility in many areas like monitoring, surveillance, commercial profiling and human-computer interaction. Security applications have utmost importance in this area. Gender classification can be used as part of a face recognition process. This paper presents a comprehensive comparison of state-of-the-art research techniques. We have divided the classification process into three stages and have presented a categorical review of existing literatures. Their analysis has been presented along-with their strengths and weaknesses. We have also discussed standard data sets. This can help the novel researcher a comprehensive review. Future dimensions are presented considering the limitations found in the literature.
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paper_title: A Study on Gait-Based Gender Classification

paper_content:

Gender is an important cue in social activities. In this correspondence, we present a study and analysis of gender classification based on human gait. Psychological experiments were carried out. These experiments showed that humans can recognize gender based on gait information, and that contributions of different body components vary. The prior knowledge extracted from the psychological experiments can be combined with an automatic method to further improve classification accuracy. The proposed method which combines human knowledge achieves higher performance than some other methods, and is even more accurate than human observers. We also present a numerical analysis of the contributions of different human components, which shows that head and hair, back, chest and thigh are more discriminative than other components. We also did challenging cross-race experiments that used Asian gait data to classify the gender of Europeans, and vice versa. Encouraging results were obtained. All the above prove that gait-based gender classification is feasible in controlled environments. In real applications, it still suffers from many difficulties, such as view variation, clothing and shoes changes, or carrying objects. We analyze the difficulties and suggest some possible solutions.
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paper_title: Vision-based Human Gender Recognition: A Survey

paper_content:

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: Advances in cross-national comparison : a European working book for demographic and socio-economic variables

paper_content:

1. Measuring Demographic and Socio-Economic Variables in Cross-National Research: An Overview C. Wolf, J.H.P. Hoffmeyer-Zlotnik. Part I: Harmonising Demographic and Socio-Economic Variables: Objectives. 2. Harmonising Data in Official Statistics: Development, Procedures, and Data Quality M. Ehling. 3. The ISSP and its Approach to Background Variables M. Braun, R. Uher. 4. Harmonising Sampling Frames and Indicators in International Market Research: A German Perspective M. Smid, D. Hess. Part II: International Standards for Demographic and Socio-Economic Variables. 5. Harmonisation of Socio-Economic Variables in EU Statistics L. Mejer. Appendix: EU-SILC Materials from the Pilot Study in 2002 compiled by U. Warner, J.H.P. Hoffmeyer-Zlotnik. 6. The ESOMAR Standard Demographic Classification: A System of International Socio-Economic Classification of Respondents to Survey Research ESOMAR. Part III: International Instruments for Selected Variables. 7. International Classification of Status in Employment, ICSE-93 E. Hoffmann. 8. International Statistical Comparisons of Occupational and Social Structures: Problems, Possibilities and the Role of ISCO-88 E. Hoffmann. 9. Three Internationally Standardised Measures for Comparative Research on Occupational Status H.B.G. Ganzeboom, D.J. Treiman. 10. International Standard Classification of Education, ISCED 1997 UNESCO. 11. The CASMIN Educational Classification in International Comparative Research H. Brauns, S. Scherer, S. Steinmann. 12. The Classification of Education as a Sociological Background Characteristic J.H.P. Hoffmeyer-Zlotnik. Part IV: Suggested Conventions for Selected Variables for Which Comparative Instruments Do Not Exist. 13. How to Measure Sex/Gender and Age C. Wolf, J.H.P. Hoffmeyer-Zlotnik. 14. How to Measure Race and Ethnicity J.H.P. Hoffmeyer-Zlotnik. 15. How to Measure Household and Family W. Bien, H. Quellenberg. 16. How to Measure (New) Work Situations B. Langfeldt. 17. How to Measure Income U. Warner, J.H.P. Hoffmeyer-Zlotnik. Part V: Cross-Cultural Comparisons: Empirical Applications. 18. Using CASMIN: The Effect of Education on Wages in Britain and Germany M. Brynin. 19. Education and Unemployment Risks among Market Entrants: A Comparison of France, the United Kingdom and West Germany H. Brauns, M. Gangl, S. Scherer. 20. The Validity of Income Measurements in Comparative Perspective: Non-Responses and Biases C. Holst. Part VI: Harmonising Demographic and Socio-Economic Variables: Synthesis. 21. Comparing Demographic and Socio-Economic Variables Across Nations: Synthesis and Recommendations J.H.P. Hoffmeyer-Zlotnik, C. Wolf. Contributing Authors. Index.
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paper_title: Gender classification from hand shape

paper_content:

Many social interactions and services today depend on gender. In this paper, we investigate the problem of gender classification from hand shape. Our work has been motivated by studies in anthropometry and psychology suggesting that it is possible to distinguish between male and female hands by considering certain geometric features. Our system segments the hand silhouette into six different parts corresponding to the palm and fingers. To represent the geometry of each part, we use region and boundary features based on Zernike moments and Fourier descriptors. For classification, we compute the distance of a given part from two different eigenspaces, one corresponding to the male class and the other corresponding to female class. We have experimented using each part of the hand separately as well as fusing information from different parts of the hand. Using a small database containing 20 males and 20 females, we report classification results close to 98% using score-level fusion and LDA.
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paper_title: Advances in cross-national comparison : a European working book for demographic and socio-economic variables

paper_content:

1. Measuring Demographic and Socio-Economic Variables in Cross-National Research: An Overview C. Wolf, J.H.P. Hoffmeyer-Zlotnik. Part I: Harmonising Demographic and Socio-Economic Variables: Objectives. 2. Harmonising Data in Official Statistics: Development, Procedures, and Data Quality M. Ehling. 3. The ISSP and its Approach to Background Variables M. Braun, R. Uher. 4. Harmonising Sampling Frames and Indicators in International Market Research: A German Perspective M. Smid, D. Hess. Part II: International Standards for Demographic and Socio-Economic Variables. 5. Harmonisation of Socio-Economic Variables in EU Statistics L. Mejer. Appendix: EU-SILC Materials from the Pilot Study in 2002 compiled by U. Warner, J.H.P. Hoffmeyer-Zlotnik. 6. The ESOMAR Standard Demographic Classification: A System of International Socio-Economic Classification of Respondents to Survey Research ESOMAR. Part III: International Instruments for Selected Variables. 7. International Classification of Status in Employment, ICSE-93 E. Hoffmann. 8. International Statistical Comparisons of Occupational and Social Structures: Problems, Possibilities and the Role of ISCO-88 E. Hoffmann. 9. Three Internationally Standardised Measures for Comparative Research on Occupational Status H.B.G. Ganzeboom, D.J. Treiman. 10. International Standard Classification of Education, ISCED 1997 UNESCO. 11. The CASMIN Educational Classification in International Comparative Research H. Brauns, S. Scherer, S. Steinmann. 12. The Classification of Education as a Sociological Background Characteristic J.H.P. Hoffmeyer-Zlotnik. Part IV: Suggested Conventions for Selected Variables for Which Comparative Instruments Do Not Exist. 13. How to Measure Sex/Gender and Age C. Wolf, J.H.P. Hoffmeyer-Zlotnik. 14. How to Measure Race and Ethnicity J.H.P. Hoffmeyer-Zlotnik. 15. How to Measure Household and Family W. Bien, H. Quellenberg. 16. How to Measure (New) Work Situations B. Langfeldt. 17. How to Measure Income U. Warner, J.H.P. Hoffmeyer-Zlotnik. Part V: Cross-Cultural Comparisons: Empirical Applications. 18. Using CASMIN: The Effect of Education on Wages in Britain and Germany M. Brynin. 19. Education and Unemployment Risks among Market Entrants: A Comparison of France, the United Kingdom and West Germany H. Brauns, M. Gangl, S. Scherer. 20. The Validity of Income Measurements in Comparative Perspective: Non-Responses and Biases C. Holst. Part VI: Harmonising Demographic and Socio-Economic Variables: Synthesis. 21. Comparing Demographic and Socio-Economic Variables Across Nations: Synthesis and Recommendations J.H.P. Hoffmeyer-Zlotnik, C. Wolf. Contributing Authors. Index.
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paper_title: Human Gait Based Gender Classification Using Various Transformation Techniques

paper_content:

Gender classification such as classifying human face is only challenging for computer, but even hard for human in some cases. In our work a new novel approach is proposed to recognize gender from the face image. Continuous Wavelet Transforms, Discrete Wavelet Transforms, Radon Transforms are used for features selections for each face images of male and female. These selected features will be used to classify the face images of each Gender using Support Vector Machine with Linear Kernel. Our work use ORL database contain 100 images include both Male and Female Gender .The experimental result shows that the proposed approach (Continuous wavelet Transform and Support Vector Machine). Proposed work achieves higher performance than some other methods, and is even more accurate than human observers. We also present a numerical analysis of the contributions of different human components, which shows that head, hair and back are more discriminative than other components. All the above prove that gait-based gender classification is feasible in controlled environments. We use both static and dynamic data sets ( images & video) for the gender classification process through SVM classifier.
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paper_title: Learning to predict gender from iris images

paper_content:

This paper employs machine learning techniques to develop models that predict gender based on the iris texture features. While there is a large body of research that explores biometrics as a means of verifying identity, there has been very little work done to determine if biometric measures can be used to determine specific human attributes. If it is possible to discover such attributes, they would be useful in situations where a biometric system fails to identify an individual that has not been enrolled, yet still needs to be identified. The iris was selected as the biometric to analyze for two major reasons: (1) quality methods have already been developed to segment and encode an iris image, (2) current iris encoding methods are conducive to selecting and extracting attributes from an iris texture and creating a meaningful feature vector.
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paper_title: A Study on Gait-Based Gender Classification

paper_content:

Gender is an important cue in social activities. In this correspondence, we present a study and analysis of gender classification based on human gait. Psychological experiments were carried out. These experiments showed that humans can recognize gender based on gait information, and that contributions of different body components vary. The prior knowledge extracted from the psychological experiments can be combined with an automatic method to further improve classification accuracy. The proposed method which combines human knowledge achieves higher performance than some other methods, and is even more accurate than human observers. We also present a numerical analysis of the contributions of different human components, which shows that head and hair, back, chest and thigh are more discriminative than other components. We also did challenging cross-race experiments that used Asian gait data to classify the gender of Europeans, and vice versa. Encouraging results were obtained. All the above prove that gait-based gender classification is feasible in controlled environments. In real applications, it still suffers from many difficulties, such as view variation, clothing and shoes changes, or carrying objects. We analyze the difficulties and suggest some possible solutions.
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paper_title: Gender Classification Using Local Directional Pattern (LDP)

paper_content:

In this paper, we present a novel texture descriptor Local Directional Pattern (LDP) to represent facial image for gender classification. The face area is divided into small regions, from which LDP histograms are extracted and concatenated into a single vector to efficiently represent the face image. The classification is performed by using support vector machines (SVMs), which had been shown to be superior to traditional pattern classifiers in gender classification problem. Experimental results show the superiority of the proposed method on the images collected from FERET face database and achieved 95.05% accuracy.
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paper_title: Gender Classification Using Ear Biometrics

paper_content:

In this work, ear biometrics has been used for gender classification. Identifying a person as male or female is an interesting problem and is required in many practical applications. The earhole has been considered as the primary reference point. Relative distances (Euclidean distance) have been measured between the ear identification points (ear features) and the ear hole. The ear features considered are outer lobe edge, outer and inner curves of the helix, outer and inner curves of the antihelix and two edges of the concha. We have used an extensive internal database of about 342 samples of male and female ears. The Bayes classifier, K-Nearest Neighbour (KNN) classifier and the neural network classifier have been used for the classification. Overall classification rate of 90.42 % is achieved using KNN classifier.
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paper_title: Gender Classification from ECG Signal Analysis using Least Square Support Vector Machine

paper_content:

In this present paper it deals with the Gender Classification fro m ECG signal using Least Square Support Vector Machine (LS-SVM ) and Support Vector Machine (SVM ) Techniques. The different features extracted fro m ECG signal using Heart Rate Variability (HRV) analysis are the input to the LS-SVM and SVM classifier and at the output the classifier, classifies whether the patient corresponding to required ECG is male or female. The least square formulation of support vector machine (SVM ) has been derived fro m statistical learn ing theory. SVM has already been marked as a novel development by learning fro m examples based on polynomial function, neural networks, radial basis function, splines and other functions. The performance of each classifier is decided by classificat ion rate (CR). Ou r result confirms the classification ability of LS-SVM technique is much better to classify gender fro m ECG signal analysis in terms of classification rate than SVM .
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paper_title: Age and gender classification using EEG paralinguistic features

paper_content:

The effects of age and gender on EEG signal have been investigated in clinical psychophysiology. However extracting age and gender information from EEG data has not been addressed. This information is useful in building automatic systems that can classify a person in to gender or age groups based on EEG characteristics of that person, index EEG data for searching, identify or verify a person, and improve brain-computer interface systems. We propose in this paper a framework of automatic age and gender classification system using EEG data. We also propose a speech-based method to extract paralinguistic features in EEG signal that contain rich age and gender information and apply these features to improve performance of our age and gender classification system. Experimental results for system evaluation and comparison are also presented.
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paper_title: Gender classification from hand shape

paper_content:

Many social interactions and services today depend on gender. In this paper, we investigate the problem of gender classification from hand shape. Our work has been motivated by studies in anthropometry and psychology suggesting that it is possible to distinguish between male and female hands by considering certain geometric features. Our system segments the hand silhouette into six different parts corresponding to the palm and fingers. To represent the geometry of each part, we use region and boundary features based on Zernike moments and Fourier descriptors. For classification, we compute the distance of a given part from two different eigenspaces, one corresponding to the male class and the other corresponding to female class. We have experimented using each part of the hand separately as well as fusing information from different parts of the hand. Using a small database containing 20 males and 20 females, we report classification results close to 98% using score-level fusion and LDA.
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paper_title: Categorizing sex and identity from the biological motion of faces

paper_content:

Abstract Head and facial movements can provide valuable cues to identity in addition to their primary roles in communicating speech and expression [1–8]. Here we report experiments in which we have used recent motion capture and animation techniques to animate an average head [9]. These techniques have allowed the isolation of motion from other cues and have enabled us to separate rigid translations and rotations of the head from nonrigid facial motion. In particular, we tested whether human observers can judge sex and identity on the basis of this information. Results show that people can discriminate both between individuals and between males and females from motion-based information alone. Rigid head movements appear particularly useful for categorization on the basis of identity, while nonrigid motion is more useful for categorization on the basis of sex. Accuracy for both sex and identity judgements is reduced when faces are presented upside down, and this finding shows that performance is not based on low-level motion cues alone and suggests that the information is represented in an object-based motion-encoding system specialized for upright faces. Playing animations backward also reduced performance for sex judgements and emphasized the importance of direction specificity in admitting access to stored representations of characteristic male and female movements.
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paper_title: Sex Discrimination: How Do We Tell the Difference between Male and Female Faces?

paper_content:

People are remarkably accurate (approaching ceiling) at deciding whether faces are male or female, even when cues from hairstyle, makeup, and facial hair are minimised. Experiments designed to explore the perceptual basis of our ability to categorise the sex of faces are reported. Subjects were considerably less accurate when asked to judge the sex of three-dimensional (3-D) representations of faces obtained by laser-scanning, compared with a condition where photographs were taken with hair concealed and eyes closed. This suggests that cues from features such as eyebrows, and skin texture, play an important role in decisionmaking. Performance with the laser-scanned heads remained quite high with 3/4-view faces, where the 3-D shape of the face should be easiest to see, suggesting that the 3-D structure of the face is a further source of information contributing to the classification of its sex. Performance at judging the sex from photographs (with hair concealed) was disrupted if the photographs were inver...
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paper_title: In the Eye of the Beholder: The Science of Face Perception

paper_content:

1: Introduction: The face - organ of communication. 2: Light, Colour, and shape: The science of vision. 3: Physical differences between faces: Age, sex, and race. 4: The mating game: attractiveness and the sociobiology. 5: Whose face is it? How individual faces are recognised. 6: Messages from the face: Lipreading, gaze, and expression. 7: In the brain of the beholder: The neuroscience of face perception
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paper_title: Learning to predict gender from iris images

paper_content:

This paper employs machine learning techniques to develop models that predict gender based on the iris texture features. While there is a large body of research that explores biometrics as a means of verifying identity, there has been very little work done to determine if biometric measures can be used to determine specific human attributes. If it is possible to discover such attributes, they would be useful in situations where a biometric system fails to identify an individual that has not been enrolled, yet still needs to be identified. The iris was selected as the biometric to analyze for two major reasons: (1) quality methods have already been developed to segment and encode an iris image, (2) current iris encoding methods are conducive to selecting and extracting attributes from an iris texture and creating a meaningful feature vector.
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paper_title: SEXNET: A Neural Network Identifies Sex From Human Faces

paper_content:

Sex identification in animals has biological importance. Humans are good at making this determination visually, but machines have not matched this ability. A neural network was trained to discriminate sex in human faces, and performed as well as humans on a set of 90 exemplars. Images sampled at 30×30 were compressed using a 900×40×900 fully-connected back-propagation network; activities of hidden units served as input to a back-propagation "SexNet" trained to produce values of 1 for male and 0 for female faces. The network's average error rate of 8.1% compared favorably to humans, who averaged 11.6%. Some SexNet errors mimicked those of humans.
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paper_title: Differences in voice quality between men and women: Use of the long-term average spectrum (LTAS)

paper_content:

Summary The goal of this study was to determine if there are acoustical differences between male and female voices, and if there are, where exactly do these differences lie. Extended speech samples were used. The recorded readings of a text by 31 women and by 24 men were analyzed by means of the Long-term Spectrum (LTAS), extracting the amplitude values (in decibels) at intervals of 160 Hz over a range of 8 kHz. The results showed a significant difference between genders, as well as an interaction of gender and frequency level. The female voice showed greater levels of aspiration noise, located in the spectral regions corresponding to the third formant, which causes the female voice to have a more “breathy” quality than the male voice. The lower spectral tilt in the women's voices is another consequence of this presence of greater aspiration noise.
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paper_title: Human Gait Based Gender Classification Using Various Transformation Techniques

paper_content:

Gender classification such as classifying human face is only challenging for computer, but even hard for human in some cases. In our work a new novel approach is proposed to recognize gender from the face image. Continuous Wavelet Transforms, Discrete Wavelet Transforms, Radon Transforms are used for features selections for each face images of male and female. These selected features will be used to classify the face images of each Gender using Support Vector Machine with Linear Kernel. Our work use ORL database contain 100 images include both Male and Female Gender .The experimental result shows that the proposed approach (Continuous wavelet Transform and Support Vector Machine). Proposed work achieves higher performance than some other methods, and is even more accurate than human observers. We also present a numerical analysis of the contributions of different human components, which shows that head, hair and back are more discriminative than other components. All the above prove that gait-based gender classification is feasible in controlled environments. We use both static and dynamic data sets ( images & video) for the gender classification process through SVM classifier.
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paper_title: Eyebrow shape-based features for biometric recognition and gender classification: A feasibility study

paper_content:

A wide variety of applications in forensic, government, and commercial fields require reliable personal identification. However, the recognition performance is severely affected when encountering non-ideal images caused by motion blur, poor contrast, various expressions, or illumination artifacts. In this paper, we investigated the use of shape-based eyebrow features under non-ideal imaging conditions for biometric recognition and gender classification. We extracted various shape-based features from the eyebrow images and compared three different classification methods: Minimum Distance Classifier (MD), Linear Discriminant Analysis Classifier (LDA) and Support Vector Machine Classifier (SVM). The methods were tested on images from two publicly available facial image databases: The Multiple Biometric Grand Challenge (MBGC) database and the Face Recognition Grand Challenge (FRGC) database. Obtained recognition rates of 90% using the MBGC database and 75% using the FRGC database as well as gender classification recognition rates of 96% and 97% for each database respectively, suggests the shape-based eyebrow features maybe be used for biometric recognition and soft biometric classification.
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paper_title: Vision-based Human Gender Recognition: A Survey

paper_content:

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.
---
paper_title: Gender Classification Using Local Directional Pattern (LDP)

paper_content:

In this paper, we present a novel texture descriptor Local Directional Pattern (LDP) to represent facial image for gender classification. The face area is divided into small regions, from which LDP histograms are extracted and concatenated into a single vector to efficiently represent the face image. The classification is performed by using support vector machines (SVMs), which had been shown to be superior to traditional pattern classifiers in gender classification problem. Experimental results show the superiority of the proposed method on the images collected from FERET face database and achieved 95.05% accuracy.
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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: Gaussian mixture models based on the frequency spectra for human identification and illumination classification

paper_content:

The importance of Fourier domain phase in human face identification is well-established Hayes et al., (1982). It therefore seems natural that identification tools based on phase features should be very efficient. In this paper we introduce a model-based approach using Gaussian mixture models (GMM) based on phase for performing human identification. Identification is performed using a MAP estimate and we show that we are able to achieve misclassification error rates as low as 2% on a database with 65 individuals with extreme illumination variations. The proposed method is easily adaptable to deal with other distortions such as expressions and poses, and hence this establishes its robustness to intra-personal variations. Finally, we demonstrate that GMM based on the Fourier domain magnitude is effective for illumination normalization, so that near perfect identification is obtained using the reconstructed illumination-free images.
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paper_title: A Comparative Analysis of Gender Classification Techniques

paper_content:

Over the period of time, automated classification of gender has gained enormous significance and has become an active area of research. Many researchers have put a lot of effort and have produced quality research in this area. Still, there is an immense potential in this field because of its utility in many areas like monitoring, surveillance, commercial profiling and human-computer interaction. Security applications have utmost importance in this area. Gender classification can be used as part of a face recognition process. This paper presents a comprehensive comparison of state-of-the-art research techniques. We have divided the classification process into three stages and have presented a categorical review of existing literatures. Their analysis has been presented along-with their strengths and weaknesses. We have also discussed standard data sets. This can help the novel researcher a comprehensive review. Future dimensions are presented considering the limitations found in the literature.
---
paper_title: Least Squares Support Vector Machine Classifiers

paper_content:

In this letter we discuss a least squares version for support vector machine (SVM) classifiers. Due to equality type constraints in the formulation, the solution follows from solving a set of linear equations, instead of quadratic programming for classical SVM‘s. The approach is illustrated on a two-spiral benchmark classification problem.
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paper_title: K-Nearest Neighbor

paper_content:

In a helical compressor, a helical compression unit is accommodated in the case. The compression unit has a cylinder, a roller eccentrically disposed in the cylinder and a helical blade disposed between an inner surface of the cylinder and the roller for separately forming a plurality of compression chambers therebetween along an axial direction of the roller. A motor unit is accommodated in the case and is operatively connected to the helical compression unit through a rotating shaft for eccentrically rotating the roller in the cylinder through the rotating shaft. According to the rotation of the roller, compressive fluid sucked into a lower compression chamber is moved along an axial direction of the rotating shaft while being sequentially compressed. The helical compression unit and the motor unit is arranged so that the helical compression unit and the motor unit are partially overlapped to each other along the axial direction.
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paper_title: Gender Classification from ECG Signal Analysis using Least Square Support Vector Machine

paper_content:

In this present paper it deals with the Gender Classification fro m ECG signal using Least Square Support Vector Machine (LS-SVM ) and Support Vector Machine (SVM ) Techniques. The different features extracted fro m ECG signal using Heart Rate Variability (HRV) analysis are the input to the LS-SVM and SVM classifier and at the output the classifier, classifies whether the patient corresponding to required ECG is male or female. The least square formulation of support vector machine (SVM ) has been derived fro m statistical learn ing theory. SVM has already been marked as a novel development by learning fro m examples based on polynomial function, neural networks, radial basis function, splines and other functions. The performance of each classifier is decided by classificat ion rate (CR). Ou r result confirms the classification ability of LS-SVM technique is much better to classify gender fro m ECG signal analysis in terms of classification rate than SVM .
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paper_title: Gender classification in two Emotional Speech databases

paper_content:

Gender classification is a challenging problem, which finds applications in speaker indexing, speaker recognition, speaker diarization, annotation and retrieval of multimedia databases, voice synthesis, smart human-computer interaction, biometrics, social robots etc. Although it has been studied for more than thirty years, by no means it is a solved problem. Processing emotional speech in order to identify speakerpsilas gender makes the problem even more interesting. A large pool of 1379 features is created including 605 novel features. A branch and bound feature selection algorithm is applied to select a subset of 15 features among the 1379 originally extracted. Support vector machines with various kernels are tested as gender classifiers, when applied to two databases, namely: the Berlin database of Emotional Speech and the Danish Emotional Speech database. The reported classification results out perform those obtained by state-of-the-art techniques, since a perfect classification accuracy is obtained.
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paper_title: Gender classification from hand shape

paper_content:

Many social interactions and services today depend on gender. In this paper, we investigate the problem of gender classification from hand shape. Our work has been motivated by studies in anthropometry and psychology suggesting that it is possible to distinguish between male and female hands by considering certain geometric features. Our system segments the hand silhouette into six different parts corresponding to the palm and fingers. To represent the geometry of each part, we use region and boundary features based on Zernike moments and Fourier descriptors. For classification, we compute the distance of a given part from two different eigenspaces, one corresponding to the male class and the other corresponding to female class. We have experimented using each part of the hand separately as well as fusing information from different parts of the hand. Using a small database containing 20 males and 20 females, we report classification results close to 98% using score-level fusion and LDA.
---
paper_title: Footwear for Gender Recognition

paper_content:

Conventionally, biometrics resources, such as face, gait silhouette, footprint, and pressure, have been utilized in gender recognition systems. However, the acquisition and processing time of these biometrics data makes the analysis difficult. This letter demonstrates for the first time how effective the footwear appearance is for gender recognition as a biometrics resource. A footwear database is also established with reprehensive shoes (footwears). Preliminary experimental results suggest that footwear appearance is a promising resource for gender recognition. Moreover, it also has the potential to be used jointly with other developed biometrics resources to boost performance.
---
paper_title: Combining appearance and motion for face and gender recognition from videos

paper_content:

While many works consider moving faces only as collections of frames and apply still image-based methods, recent developments indicate that excellent results can be obtained using texture-based spatiotemporal representations for describing and analyzing faces in videos. Inspired by the psychophysical findings which state that facial movements can provide valuable information to face analysis, and also by our recent success in using LBP (local binary patterns) for combining appearance and motion for dynamic texture analysis, this paper investigates the combination of facial appearance (the shape of the face) and motion (the way a person is talking and moving his/her facial features) for face analysis in videos. We propose and study an approach for spatiotemporal face and gender recognition from videos using an extended set of volume LBP features and a boosting scheme. We experiment with several publicly available video face databases and consider different benchmark methods for comparison. Our extensive experimental analysis clearly assesses the promising performance of the LBP-based spatiotemporal representations for describing and analyzing faces in videos.
---
paper_title: Learning to predict gender from iris images

paper_content:

This paper employs machine learning techniques to develop models that predict gender based on the iris texture features. While there is a large body of research that explores biometrics as a means of verifying identity, there has been very little work done to determine if biometric measures can be used to determine specific human attributes. If it is possible to discover such attributes, they would be useful in situations where a biometric system fails to identify an individual that has not been enrolled, yet still needs to be identified. The iris was selected as the biometric to analyze for two major reasons: (1) quality methods have already been developed to segment and encode an iris image, (2) current iris encoding methods are conducive to selecting and extracting attributes from an iris texture and creating a meaningful feature vector.
---
paper_title: The role of voice source measures on automatic gender classification

paper_content:

Differences of physiological properties of the glottis and the vocal tract are partly due to age and/or gender differences. Since these differences are reflected in the speech signal, acoustic measures related to those properties can be helpful for automatic age and gender classification. In this paper, the focus is on the role of acoustic measures related to the voice source in automatic gender classification, implemented using support vector machines (SVMs). Acoustic measures of the vocal tract and the voice source were extracted from 3880 utterances spoken by 205 male and 160 female talkers (aged 8 to 39 years old). Formant frequencies and formant bandwidths were used as vocal tract measures, and open quotient and source spectral tilt correlates were used as voice source measures. Results show that the addition of voice source measures can help improve automatic gender classification results for most age groups.
---
paper_title: Improving Gender Classification of Blog Authors

paper_content:

The problem of automatically classifying the gender of a blog author has important applications in many commercial domains. Existing systems mainly use features such as words, word classes, and POS (part-of-speech) n-grams, for classification learning. In this paper, we propose two new techniques to improve the current result. The first technique introduces a new class of features which are variable length POS sequence patterns mined from the training data using a sequence pattern mining algorithm. The second technique is a new feature selection method which is based on an ensemble of several feature selection criteria and approaches. Empirical evaluation using a real-life blog data set shows that these two techniques improve the classification accuracy of the current state-of-the-art methods significantly.
---
paper_title: Fingerprint-Based Gender Classification

paper_content:

Gender classification from fingerprints is an important step in forensic anthropology in order to identify the gender of a criminal and minimize the list of suspects search. A dataset of 10-fingerprint images for 2200 persons of different ages and gender (1100 males and 1100 females) was analyzed. Features extracted were; ridge count, ridge thickness to valley thickness ratio (RTVTR), white lines count, ridge count asymmetry, and pattern type concordance. Fuzzy CMeans (FCM), Linear Discriminant Analysis (LDA), and Neural Network (NN) were used for the classification using the most dominant features. We obtained results of 80.39%, 86.5%, and 88.5% using FCM, LDA, and NN, respectively. Results of this analysis make this method a prime candidate to utilize in forensic anthropology for gender classification in order to minimize the suspects search list by getting a likelihood value for the criminal gender.
---
paper_title: The effects of age and gender on sleep EEG power spectral density in the middle years of life (ages 20-60 years old)

paper_content:

The effects of age and gender on sleep EEG power spectral density were assessed in a group of 100 subjects aged 20 to 60 years. We propose a new statistical strategy (mixed-model using fixed-knot regression splines) to analyze quantitative EEG measures. The effect of gender varied according to frequency, but no interactions emerged between age and gender, suggesting that the aging process does not differentially influence men and women. Women had higher power density than men in delta, theta, low alpha, and high spindle frequency range. The effect of age varied according to frequency and across the night. The decrease in power with age was not restricted to slow-wave activity, but also included theta and sigma activity. With increasing age, the attenuation over the night in power density between 1.25 and 8.00 Hz diminished, and the rise in power between 12.25 and 14.00 Hz across the night decreased. Increasing age was associated with higher power in the beta range. These results suggest that increasing age may be related to an attenuation of homeostatic sleep pressure and to an increase in cortical activation during sleep.
---
paper_title: A Comparative Analysis of Gender Classification Techniques

paper_content:

Over the period of time, automated classification of gender has gained enormous significance and has become an active area of research. Many researchers have put a lot of effort and have produced quality research in this area. Still, there is an immense potential in this field because of its utility in many areas like monitoring, surveillance, commercial profiling and human-computer interaction. Security applications have utmost importance in this area. Gender classification can be used as part of a face recognition process. This paper presents a comprehensive comparison of state-of-the-art research techniques. We have divided the classification process into three stages and have presented a categorical review of existing literatures. Their analysis has been presented along-with their strengths and weaknesses. We have also discussed standard data sets. This can help the novel researcher a comprehensive review. Future dimensions are presented considering the limitations found in the literature.
---
paper_title: Eyebrow shape-based features for biometric recognition and gender classification: A feasibility study

paper_content:

A wide variety of applications in forensic, government, and commercial fields require reliable personal identification. However, the recognition performance is severely affected when encountering non-ideal images caused by motion blur, poor contrast, various expressions, or illumination artifacts. In this paper, we investigated the use of shape-based eyebrow features under non-ideal imaging conditions for biometric recognition and gender classification. We extracted various shape-based features from the eyebrow images and compared three different classification methods: Minimum Distance Classifier (MD), Linear Discriminant Analysis Classifier (LDA) and Support Vector Machine Classifier (SVM). The methods were tested on images from two publicly available facial image databases: The Multiple Biometric Grand Challenge (MBGC) database and the Face Recognition Grand Challenge (FRGC) database. Obtained recognition rates of 90% using the MBGC database and 75% using the FRGC database as well as gender classification recognition rates of 96% and 97% for each database respectively, suggests the shape-based eyebrow features maybe be used for biometric recognition and soft biometric classification.
---
paper_title: Gender recognition from body

paper_content:

This paper studies the problem of recognizing gender from full body images. This problem has not been addressed before, partly because of the variant nature of human bodies and clothing that can bring tough difficulties. However, gender recognition has high application potentials, e.g. security surveillance and customer statistics collection in restaurants, supermarkets, and even building entrances. In this paper, we build a system of recognizing gender from full body images, taken from frontal or back views. Our contributions are three-fold. First, to handle the variety of human body characteristics, we represent each image by a collection of patch features, which model different body parts and provide a set of clues for gender recognition. To combine the clues, we build an ensemble learning algorithm from those body parts to recognize gender from fixed view body images (frontal or back). Second, we relax the fixed view constraint and show the possibility to train a flexible classifier for mixed view images with the almost same accuracy as the fixed view case. At last, our approach is shown to be robust to small alignment errors, which is preferred in many applications.
---
paper_title: Application of DNA techniques for identification using human dental pulp as a source of DNA

paper_content:

Dental pulp tissue could be obtained in most cases from materials obtained under experimental conditions and from forensic casework (air accidents, burned and putrefied bodies). Teeth extracted during dental treatment (n = 30) were stored for 6 weeks and 4 years at room temperature. In addition teeth (n = 10) extracted from jaw fragments that had been stored for 15 years at room temperature, and teeth extracted post mortem from actual identification cases (n = 8) were investigated. Following extraction from dental pulp tissue the DNA concentration was measured by fluorometry. The amount of DNA obtained from the dental pulp tissue of a single tooth varied from 6 micrograms to 50 micrograms DNA. In most cases high molecular weight DNA was still present although the major portion consisted of degraded DNA. Genomic dot blot hybridization for sex determination using the biotinylated repetitive DNA probe pHY 2.1 was performed and sex was correctly classified in all cases using 50-100 ng target DNA. PCR typing of the HLA-DQ alpha and ApoB 3' VNTR systems from dental pulp tissue DNA was in agreement with the results obtained from blood, bloodstains, or lung tissue. In addition, Southern blot analysis of selected samples using the single locus VNTR probe pYNH24 was successfully performed. In all cases the DNA recovered from dental pulp was unsuitable for multilocus probe analysis.
---
paper_title: A Study on Gait-Based Gender Classification

paper_content:

Gender is an important cue in social activities. In this correspondence, we present a study and analysis of gender classification based on human gait. Psychological experiments were carried out. These experiments showed that humans can recognize gender based on gait information, and that contributions of different body components vary. The prior knowledge extracted from the psychological experiments can be combined with an automatic method to further improve classification accuracy. The proposed method which combines human knowledge achieves higher performance than some other methods, and is even more accurate than human observers. We also present a numerical analysis of the contributions of different human components, which shows that head and hair, back, chest and thigh are more discriminative than other components. We also did challenging cross-race experiments that used Asian gait data to classify the gender of Europeans, and vice versa. Encouraging results were obtained. All the above prove that gait-based gender classification is feasible in controlled environments. In real applications, it still suffers from many difficulties, such as view variation, clothing and shoes changes, or carrying objects. We analyze the difficulties and suggest some possible solutions.
---
paper_title: A method of gender classification by integrating facial, hairstyle, and clothing images

paper_content:

This work presents a method of gender classification by integrating facial, hairstyle, and clothing images. Initially, input images are separated into facial, hairstyle and clothing regions, and independently learned PCAs and GMMs based on thousands of sample images are applied to each region. The classification results are then integrated into a single score using some known priors based on the Bayes rule. Experimental results showed that our integration strategy significantly reduced error rate in gender classification compared with the conventional facial only approach.
---
paper_title: Age and sex effects in the EEG: development of the normal child

paper_content:

Abstract Objectives : This study investigated age-related changes and sex differences in the EEGs of normal children. Methods : Forty boys and 40 girls, between the ages of 8 and 12 years, participated in this study. The EEG was recorded during an eyes-closed resting condition and Fourier transformed to provide estimates for total power, absolute and relative power in the delta, theta, alpha and beta bands, and for theta/alpha and theta/beta ratios. Results : Absolute delta activity decreased with age. Relative delta and theta decreased and alpha and beta increased with increasing age. The theta/alpha and theta/beta ratios decreased with increasing age. All of these indicated a developmental reduction in slow wave activity. Maturational differences were found in the rates of change between the midline and the two hemispheres. In the absolute delta and the theta/beta ratio, the midline and the two hemispheres became more equipotential with age. In the beta band, power increased at a greater rate than in the two hemispheres. Sex differences were found, with males having less theta and more alpha than females. Conclusions : These results indicated that maturation occurs earlier at the midline than in the two hemispheres. Females were also found to have a developmental lag in the EEG compared with males.
---
paper_title: Robust gender recognition by exploiting facial attributes dependencies

paper_content:

Estimating human face gender from images is a problem that has been extensively studied because of its relevant applications. Recent works report significant drops in performance for state-of-the-art gender classifiers when evaluated ''in the wild,'' i.e., with uncontrolled demography and environmental conditions. We hypothesize that this is caused by the existence of dependencies among facial demographic attributes that have not been considered when building the classifier. In the paper we study the dependencies among gender, age and pose facial attributes. By considering the relation between gender and pose attributes we also avoid the use of computationally expensive and fragile face alignment procedures. In the experiments we confirm the existence of dependencies among gender, age and pose facial attributes and prove that we can improve the performance and robustness of gender classifiers by exploiting these dependencies.
---
paper_title: Learning local binary patterns for gender classification on real-world face images

paper_content:

Gender recognition is one of fundamental face analysis tasks. Most of the existing studies have focused on face images acquired under controlled conditions. However, real-world applications require gender classification on real-life faces, which is much more challenging due to significant appearance variations in unconstrained scenarios. In this paper, we investigate gender recognition on real-life faces using the recently built database, the Labeled Faces in the Wild (LFW). Local Binary Patterns (LBP) is employed to describe faces, and Adaboost is used to select the discriminative LBP features. We obtain the performance of 94.81% by applying Support Vector Machine (SVM) with the boosted LBP features. The public database used in this study makes future benchmark and evaluation possible.
---
paper_title: Gender classification from hand shape

paper_content:

Many social interactions and services today depend on gender. In this paper, we investigate the problem of gender classification from hand shape. Our work has been motivated by studies in anthropometry and psychology suggesting that it is possible to distinguish between male and female hands by considering certain geometric features. Our system segments the hand silhouette into six different parts corresponding to the palm and fingers. To represent the geometry of each part, we use region and boundary features based on Zernike moments and Fourier descriptors. For classification, we compute the distance of a given part from two different eigenspaces, one corresponding to the male class and the other corresponding to female class. We have experimented using each part of the hand separately as well as fusing information from different parts of the hand. Using a small database containing 20 males and 20 females, we report classification results close to 98% using score-level fusion and LDA.
---
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.
---
paper_title: A Comparative Analysis of Gender Classification Techniques

paper_content:

Over the period of time, automated classification of gender has gained enormous significance and has become an active area of research. Many researchers have put a lot of effort and have produced quality research in this area. Still, there is an immense potential in this field because of its utility in many areas like monitoring, surveillance, commercial profiling and human-computer interaction. Security applications have utmost importance in this area. Gender classification can be used as part of a face recognition process. This paper presents a comprehensive comparison of state-of-the-art research techniques. We have divided the classification process into three stages and have presented a categorical review of existing literatures. Their analysis has been presented along-with their strengths and weaknesses. We have also discussed standard data sets. This can help the novel researcher a comprehensive review. Future dimensions are presented considering the limitations found in the literature.
---
paper_title: Eyebrow shape-based features for biometric recognition and gender classification: A feasibility study

paper_content:

A wide variety of applications in forensic, government, and commercial fields require reliable personal identification. However, the recognition performance is severely affected when encountering non-ideal images caused by motion blur, poor contrast, various expressions, or illumination artifacts. In this paper, we investigated the use of shape-based eyebrow features under non-ideal imaging conditions for biometric recognition and gender classification. We extracted various shape-based features from the eyebrow images and compared three different classification methods: Minimum Distance Classifier (MD), Linear Discriminant Analysis Classifier (LDA) and Support Vector Machine Classifier (SVM). The methods were tested on images from two publicly available facial image databases: The Multiple Biometric Grand Challenge (MBGC) database and the Face Recognition Grand Challenge (FRGC) database. Obtained recognition rates of 90% using the MBGC database and 75% using the FRGC database as well as gender classification recognition rates of 96% and 97% for each database respectively, suggests the shape-based eyebrow features maybe be used for biometric recognition and soft biometric classification.
---
paper_title: Gender recognition from body

paper_content:

This paper studies the problem of recognizing gender from full body images. This problem has not been addressed before, partly because of the variant nature of human bodies and clothing that can bring tough difficulties. However, gender recognition has high application potentials, e.g. security surveillance and customer statistics collection in restaurants, supermarkets, and even building entrances. In this paper, we build a system of recognizing gender from full body images, taken from frontal or back views. Our contributions are three-fold. First, to handle the variety of human body characteristics, we represent each image by a collection of patch features, which model different body parts and provide a set of clues for gender recognition. To combine the clues, we build an ensemble learning algorithm from those body parts to recognize gender from fixed view body images (frontal or back). Second, we relax the fixed view constraint and show the possibility to train a flexible classifier for mixed view images with the almost same accuracy as the fixed view case. At last, our approach is shown to be robust to small alignment errors, which is preferred in many applications.
---
paper_title: Gender classification by combining clothing, hair and facial component classifiers

paper_content:

In this paper, we propose a novel gender classification framework, which utilizes not only facial features, but also external information, i.e. hair and clothing. Instead of using the whole face, we consider five facial components: forehead, eyes, nose, mouth and chin. We also design feature extraction methods for hair and clothing; these features have seldom been used in previous work because of their large variability. For each type of feature, we train a single support vector machine classifier with probabilistic output. The outputs of these classifiers are combined using various strategies, namely fuzzy integral, maximal, sum, voting, and product rule. The major contributions of this paper are (1) investigating the gender discriminative ability of clothing information; (2) using facial components instead of the whole face to obtain higher robustness for occlusions and noise; (3) exploiting hair and clothing information to facilitate gender classification. Experimental results show that our proposed framework improves classification accuracy, even when images contain occlusions, noise, and illumination changes.
---
paper_title: Overview of the Multiple Biometrics Grand Challenge

paper_content:

The goal of the Multiple Biometrics Grand Challenge (MBGC) is to improve the performance of face and iris recognition technology from biometric samples acquired under unconstrained conditions. The MBGC is organized into three challenge problems. Each challenge problem relaxes the acquisition constraints in different directions. In the Portal Challenge Problem, the goal is to recognize people from near-infrared (NIR) and high definition (HD) video as they walk through a portal. Iris recognition can be performed from the NIR video and face recognition from the HD video. The availability of NIR and HD modalities allows for the development of fusion algorithms. The Still Face Challenge Problem has two primary goals. The first is to improve recognition performance from frontal and off angle still face images taken under uncontrolled indoor and outdoor lighting. The second is to improve recognition performance on still frontal face images that have been resized and compressed, as is required for electronic passports. In the Video Challenge Problem, the goal is to recognize people from video in unconstrained environments. The video is unconstrained in pose, illumination, and camera angle. All three challenge problems include a large data set, experiment descriptions, ground truth, and scoring code.
---
paper_title: Combining appearance and motion for face and gender recognition from videos

paper_content:

While many works consider moving faces only as collections of frames and apply still image-based methods, recent developments indicate that excellent results can be obtained using texture-based spatiotemporal representations for describing and analyzing faces in videos. Inspired by the psychophysical findings which state that facial movements can provide valuable information to face analysis, and also by our recent success in using LBP (local binary patterns) for combining appearance and motion for dynamic texture analysis, this paper investigates the combination of facial appearance (the shape of the face) and motion (the way a person is talking and moving his/her facial features) for face analysis in videos. We propose and study an approach for spatiotemporal face and gender recognition from videos using an extended set of volume LBP features and a boosting scheme. We experiment with several publicly available video face databases and consider different benchmark methods for comparison. Our extensive experimental analysis clearly assesses the promising performance of the LBP-based spatiotemporal representations for describing and analyzing faces in videos.
---
paper_title: A Study on Gait-Based Gender Classification

paper_content:

Gender is an important cue in social activities. In this correspondence, we present a study and analysis of gender classification based on human gait. Psychological experiments were carried out. These experiments showed that humans can recognize gender based on gait information, and that contributions of different body components vary. The prior knowledge extracted from the psychological experiments can be combined with an automatic method to further improve classification accuracy. The proposed method which combines human knowledge achieves higher performance than some other methods, and is even more accurate than human observers. We also present a numerical analysis of the contributions of different human components, which shows that head and hair, back, chest and thigh are more discriminative than other components. We also did challenging cross-race experiments that used Asian gait data to classify the gender of Europeans, and vice versa. Encouraging results were obtained. All the above prove that gait-based gender classification is feasible in controlled environments. In real applications, it still suffers from many difficulties, such as view variation, clothing and shoes changes, or carrying objects. We analyze the difficulties and suggest some possible solutions.
---
paper_title: Footwear for Gender Recognition

paper_content:

Conventionally, biometrics resources, such as face, gait silhouette, footprint, and pressure, have been utilized in gender recognition systems. However, the acquisition and processing time of these biometrics data makes the analysis difficult. This letter demonstrates for the first time how effective the footwear appearance is for gender recognition as a biometrics resource. A footwear database is also established with reprehensive shoes (footwears). Preliminary experimental results suggest that footwear appearance is a promising resource for gender recognition. Moreover, it also has the potential to be used jointly with other developed biometrics resources to boost performance.
---
paper_title: A method of gender classification by integrating facial, hairstyle, and clothing images

paper_content:

This work presents a method of gender classification by integrating facial, hairstyle, and clothing images. Initially, input images are separated into facial, hairstyle and clothing regions, and independently learned PCAs and GMMs based on thousands of sample images are applied to each region. The classification results are then integrated into a single score using some known priors based on the Bayes rule. Experimental results showed that our integration strategy significantly reduced error rate in gender classification compared with the conventional facial only approach.
---
paper_title: Combining appearance and motion for face and gender recognition from videos

paper_content:

While many works consider moving faces only as collections of frames and apply still image-based methods, recent developments indicate that excellent results can be obtained using texture-based spatiotemporal representations for describing and analyzing faces in videos. Inspired by the psychophysical findings which state that facial movements can provide valuable information to face analysis, and also by our recent success in using LBP (local binary patterns) for combining appearance and motion for dynamic texture analysis, this paper investigates the combination of facial appearance (the shape of the face) and motion (the way a person is talking and moving his/her facial features) for face analysis in videos. We propose and study an approach for spatiotemporal face and gender recognition from videos using an extended set of volume LBP features and a boosting scheme. We experiment with several publicly available video face databases and consider different benchmark methods for comparison. Our extensive experimental analysis clearly assesses the promising performance of the LBP-based spatiotemporal representations for describing and analyzing faces in videos.
---
paper_title: Gender classification in two Emotional Speech databases

paper_content:

Gender classification is a challenging problem, which finds applications in speaker indexing, speaker recognition, speaker diarization, annotation and retrieval of multimedia databases, voice synthesis, smart human-computer interaction, biometrics, social robots etc. Although it has been studied for more than thirty years, by no means it is a solved problem. Processing emotional speech in order to identify speakerpsilas gender makes the problem even more interesting. A large pool of 1379 features is created including 605 novel features. A branch and bound feature selection algorithm is applied to select a subset of 15 features among the 1379 originally extracted. Support vector machines with various kernels are tested as gender classifiers, when applied to two databases, namely: the Berlin database of Emotional Speech and the Danish Emotional Speech database. The reported classification results out perform those obtained by state-of-the-art techniques, since a perfect classification accuracy is obtained.
---
paper_title: Learning to predict gender from iris images

paper_content:

This paper employs machine learning techniques to develop models that predict gender based on the iris texture features. While there is a large body of research that explores biometrics as a means of verifying identity, there has been very little work done to determine if biometric measures can be used to determine specific human attributes. If it is possible to discover such attributes, they would be useful in situations where a biometric system fails to identify an individual that has not been enrolled, yet still needs to be identified. The iris was selected as the biometric to analyze for two major reasons: (1) quality methods have already been developed to segment and encode an iris image, (2) current iris encoding methods are conducive to selecting and extracting attributes from an iris texture and creating a meaningful feature vector.
---
paper_title: The role of voice source measures on automatic gender classification

paper_content:

Differences of physiological properties of the glottis and the vocal tract are partly due to age and/or gender differences. Since these differences are reflected in the speech signal, acoustic measures related to those properties can be helpful for automatic age and gender classification. In this paper, the focus is on the role of acoustic measures related to the voice source in automatic gender classification, implemented using support vector machines (SVMs). Acoustic measures of the vocal tract and the voice source were extracted from 3880 utterances spoken by 205 male and 160 female talkers (aged 8 to 39 years old). Formant frequencies and formant bandwidths were used as vocal tract measures, and open quotient and source spectral tilt correlates were used as voice source measures. Results show that the addition of voice source measures can help improve automatic gender classification results for most age groups.
---
paper_title: Fingerprint-Based Gender Classification

paper_content:

Gender classification from fingerprints is an important step in forensic anthropology in order to identify the gender of a criminal and minimize the list of suspects search. A dataset of 10-fingerprint images for 2200 persons of different ages and gender (1100 males and 1100 females) was analyzed. Features extracted were; ridge count, ridge thickness to valley thickness ratio (RTVTR), white lines count, ridge count asymmetry, and pattern type concordance. Fuzzy CMeans (FCM), Linear Discriminant Analysis (LDA), and Neural Network (NN) were used for the classification using the most dominant features. We obtained results of 80.39%, 86.5%, and 88.5% using FCM, LDA, and NN, respectively. Results of this analysis make this method a prime candidate to utilize in forensic anthropology for gender classification in order to minimize the suspects search list by getting a likelihood value for the criminal gender.
---
paper_title: Fingerprint Gender Classification using Wavelet Transform and Singular Value Decomposition

paper_content:

A novel method of gender Classification from fingerprint is proposed based on discrete wavelet transform (DWT) and singular value decomposition (SVD). The classification is achieved by extracting the energy computed from all the sub-bands of DWT combined with the spatial features of non-zero singular values obtained from the SVD of fingerprint images. K nearest neighbor (KNN) used as a classifier. This method is experimented with the internal database of 3570 fingerprints finger prints in which 1980 were male fingerprints and 1590 were female fingerprints. Finger-wise gender classification is achieved which is 94.32% for the left hand little fingers of female persons and 95.46% for the left hand index finger of male persons. Gender classification for any finger of male persons tested is attained as 91.67% and 84.69% for female persons respectively. Overall classification rate is 88.28% has been achieved.
---
paper_title: Gender Classification Using Ear Biometrics

paper_content:

In this work, ear biometrics has been used for gender classification. Identifying a person as male or female is an interesting problem and is required in many practical applications. The earhole has been considered as the primary reference point. Relative distances (Euclidean distance) have been measured between the ear identification points (ear features) and the ear hole. The ear features considered are outer lobe edge, outer and inner curves of the helix, outer and inner curves of the antihelix and two edges of the concha. We have used an extensive internal database of about 342 samples of male and female ears. The Bayes classifier, K-Nearest Neighbour (KNN) classifier and the neural network classifier have been used for the classification. Overall classification rate of 90.42 % is achieved using KNN classifier.
---
paper_title: Gender Classification from ECG Signal Analysis using Least Square Support Vector Machine

paper_content:

In this present paper it deals with the Gender Classification fro m ECG signal using Least Square Support Vector Machine (LS-SVM ) and Support Vector Machine (SVM ) Techniques. The different features extracted fro m ECG signal using Heart Rate Variability (HRV) analysis are the input to the LS-SVM and SVM classifier and at the output the classifier, classifies whether the patient corresponding to required ECG is male or female. The least square formulation of support vector machine (SVM ) has been derived fro m statistical learn ing theory. SVM has already been marked as a novel development by learning fro m examples based on polynomial function, neural networks, radial basis function, splines and other functions. The performance of each classifier is decided by classificat ion rate (CR). Ou r result confirms the classification ability of LS-SVM technique is much better to classify gender fro m ECG signal analysis in terms of classification rate than SVM .
---
paper_title: Age and gender classification using EEG paralinguistic features

paper_content:

The effects of age and gender on EEG signal have been investigated in clinical psychophysiology. However extracting age and gender information from EEG data has not been addressed. This information is useful in building automatic systems that can classify a person in to gender or age groups based on EEG characteristics of that person, index EEG data for searching, identify or verify a person, and improve brain-computer interface systems. We propose in this paper a framework of automatic age and gender classification system using EEG data. We also propose a speech-based method to extract paralinguistic features in EEG signal that contain rich age and gender information and apply these features to improve performance of our age and gender classification system. Experimental results for system evaluation and comparison are also presented.
---
paper_title: Application of DNA techniques for identification using human dental pulp as a source of DNA

paper_content:

Dental pulp tissue could be obtained in most cases from materials obtained under experimental conditions and from forensic casework (air accidents, burned and putrefied bodies). Teeth extracted during dental treatment (n = 30) were stored for 6 weeks and 4 years at room temperature. In addition teeth (n = 10) extracted from jaw fragments that had been stored for 15 years at room temperature, and teeth extracted post mortem from actual identification cases (n = 8) were investigated. Following extraction from dental pulp tissue the DNA concentration was measured by fluorometry. The amount of DNA obtained from the dental pulp tissue of a single tooth varied from 6 micrograms to 50 micrograms DNA. In most cases high molecular weight DNA was still present although the major portion consisted of degraded DNA. Genomic dot blot hybridization for sex determination using the biotinylated repetitive DNA probe pHY 2.1 was performed and sex was correctly classified in all cases using 50-100 ng target DNA. PCR typing of the HLA-DQ alpha and ApoB 3' VNTR systems from dental pulp tissue DNA was in agreement with the results obtained from blood, bloodstains, or lung tissue. In addition, Southern blot analysis of selected samples using the single locus VNTR probe pYNH24 was successfully performed. In all cases the DNA recovered from dental pulp was unsuitable for multilocus probe analysis.
---
paper_title: Improving Gender Classification of Blog Authors

paper_content:

The problem of automatically classifying the gender of a blog author has important applications in many commercial domains. Existing systems mainly use features such as words, word classes, and POS (part-of-speech) n-grams, for classification learning. In this paper, we propose two new techniques to improve the current result. The first technique introduces a new class of features which are variable length POS sequence patterns mined from the training data using a sequence pattern mining algorithm. The second technique is a new feature selection method which is based on an ensemble of several feature selection criteria and approaches. Empirical evaluation using a real-life blog data set shows that these two techniques improve the classification accuracy of the current state-of-the-art methods significantly.
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Title: Human Gender Classification: A Review

Section 1: INTRODUCTION
Description 1: Provide an overview of the importance, applications, and current state of research in gender classification, including the four key requirements of qualified features and the evaluation standards.

Section 2: APPLICATIONS
Description 2: Discuss the various applications of gender classification in several fields such as human-computer interaction, surveillance systems, commercial development, demographic research, and mobile applications and video games.

Section 3: Challenges of Gender Classification
Description 3: Outline the major challenges faced in gender classification, including variation in features, image quality, and the selection of features and classification algorithms.

Section 4: Classification Framework
Description 4: Describe the general gender classification framework, including the processes of sensing, preprocessing, feature extraction, classification algorithm, and evaluation.

Section 5: Gender Classification Taxonomy
Description 5: Categorize and explain the different approaches to gender classification, including appearance-based and non-appearance-based methods.

Section 6: APPEARANCE-BASED GENDER CLASSIFICATION
Description 6: Detail the vision-based methods for gender classification using static body features, dynamic body features, and apparel features, including their advantages, limitations, and example studies.

Section 7: BIOLOGICAL INFORMATION-BASED GENDER CLASSIFICATION
Description 7: Discuss gender classification methods using biometric and bio-signal information, highlighting the advantages and limitations of each approach.

Section 8: SOCIAL NETWORK INFORMATION-BASED GENDER CLASSIFICATION
Description 8: Examine gender classification based on social network information such as handwriting, email, and blogs, noting the complexity and challenges of this approach.

Section 9: COMPARISON AND DISCUSSION
Description 9: Compare the different gender classification approaches in terms of accuracy, trustworthiness, invasiveness, collectability, and application scope.

Section 10: CONCLUSION AND FUTURE RESEARCH
Description 10: Summarize the findings of the review, discuss the limitations of existing approaches, and propose directions for future research to improve accuracy, efficiency, and the exploration of new features.