Multi-view gender classification using symmetry of facial images

In this paper, we propose a multi-view gender classification system with a hierarchical framework using facial images as input. The front end of the framework is a classifier, which will properly divides the input images into several groups. To ease the data sparsity problem in the multi-view scenario, facial symmetry is used to reduce the number of views. Moreover, we adopt soft assignment when dividing the input data, which can reduce the errors caused by the boundary effect in hard assignment. Then for each group, we train a gender classifier, called an expert. These experts can be any commonly used classifiers, such as support vector machines or neural networks. In this step, facial components instead of the whole face are used to achieve higher robustness against variations caused by facial alignment, illumination and occlusions. Experimental results demonstrate that our framework significantly improves the performance.

[1]  H. Ai,et al.  LUT-Based Adaboost for Gender Classification , 2003, AVBPA.

[2]  JEAN-MARC FELLOUS,et al.  PII: S0042-6989(97)00010-2 , 2003 .

[3]  Wen Gao,et al.  The CAS-PEAL Large-Scale Chinese Face Database and Baseline Evaluations , 2008, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[4]  Wen Gao,et al.  Local Gabor binary pattern histogram sequence (LGBPHS): a novel non-statistical model for face representation and recognition , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[5]  Brunelli Poggio,et al.  HyberBF Networks for Gender Classification , 1992 .

[6]  Timothy F. Cootes,et al.  Active Shape Models-Their Training and Application , 1995, Comput. Vis. Image Underst..

[7]  Chih-Jen Lin,et al.  Probability Estimates for Multi-class Classification by Pairwise Coupling , 2003, J. Mach. Learn. Res..

[8]  Yasue Mitsukura,et al.  A robust gender and age estimation under varying facial pose , 2007 .

[9]  Matthew Toews,et al.  Detection, Localization, and Sex Classification of Faces from Arbitrary Viewpoints and under Occlusion , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  Sushil J. Louis,et al.  Genetic feature subset selection for gender classification: a comparison study , 2002, Sixth IEEE Workshop on Applications of Computer Vision, 2002. (WACV 2002). Proceedings..

[11]  Ji Zheng,et al.  A support vector machine classifier with automatic confidence and its application to gender classification , 2011, Neurocomputing.

[12]  Harry Wechsler,et al.  Face pose discrimination using support vector machines (SVM) , 1998, Proceedings. Fourteenth International Conference on Pattern Recognition (Cat. No.98EX170).

[13]  Bao-Liang Lu,et al.  Multi-View Gender Classification Using Multi-Resolution Local Binary Patterns and Support Vector Machines , 2007, Int. J. Neural Syst..

[14]  S. V. N. Vishwanathan,et al.  Multiple Kernel Learning and the SMO Algorithm , 2010, NIPS.

[15]  Gang Tao,et al.  A Comparison Study , 2003 .

[16]  Bao-Liang Lu,et al.  Gender Classification by Combining Facial and Hair Information , 2009, ICONIP.

[17]  Bao-Liang Lu,et al.  Multi-view Gender Classification Using Local Binary Patterns and Support Vector Machines , 2006, ISNN.

[18]  Shinichi Tamura,et al.  Male/female identification from 8×6 very low resolution face images by neural network , 1996, Pattern Recognit..

[19]  Hyun-Chul Kim,et al.  Appearance-based gender classification with Gaussian processes , 2006, Pattern Recognit. Lett..

[20]  Garrison W. Cottrell,et al.  EMPATH: Face, Emotion, and Gender Recognition Using Holons , 1990, NIPS.

[21]  Terrence J. Sejnowski,et al.  SEXNET: A Neural Network Identifies Sex From Human Faces , 1990, NIPS.

[22]  Harry Wechsler,et al.  Gender and ethnic classification of human faces using hybrid classifiers , 1999, IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339).

[23]  Changyin Sun,et al.  Gender Classification Based on Boosting Local Binary Pattern , 2006, ISNN.

[24]  Yasue Mitsukura,et al.  Robust gender and age estimation under varying facial pose , 2008 .

[25]  Amit Jain,et al.  Integrating independent components and linear discriminant analysis for gender classification , 2004, Sixth IEEE International Conference on Automatic Face and Gesture Recognition, 2004. Proceedings..

[26]  D. Perrett,et al.  What Gives a Face its Gender? , 1993, Perception.

[27]  Ammad Ali,et al.  Face Recognition with Local Binary Patterns , 2012 .

[28]  M. Sugeno,et al.  An interpretation of fuzzy measures and the Choquet integral as an integral with respect to a fuzzy , 1989 .

[29]  Ming-Hsuan Yang,et al.  Learning Gender with Support Faces , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[30]  菅野 道夫,et al.  Theory of fuzzy integrals and its applications , 1975 .

[31]  Roope Raisamo,et al.  Evaluation of Gender Classification Methods with Automatically Detected and Aligned Faces , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[32]  A. M. Burton,et al.  What's the Difference between Men and Women? Evidence from Facial Measurement , 1993, Perception.

[33]  Shumeet Baluja,et al.  Boosting Sex Identification Performance , 2005, International Journal of Computer Vision.

[34]  Bin Xia,et al.  Multi-view gender classification based on local Gabor binary mapping pattern and support vector machines , 2008, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).

[35]  David,et al.  Pose Discriminiation and Eye Detection Using Support Vector Machines (SVM) , 1998 .