Gender Classification Using Facial Images and Basis Pursuit

In many social interactions, it is important to correctly recognize the gender. Researches have addressed this issue based on facial images, ear images and gait. In this paper, we present an approach for gender classification using facial images based upon sparse representation and Basis Pursuit. In sparse representation, the training data is used to develop a dictionary based on extracted features. Classification is achieved by representing the extracted features of the test data using the dictionary. For this purpose, basis pursuit is used to find the best representation by minimizing the l 1 norm. In this work, Gabor filters are used for feature extraction. Experimental results are conducted on the FERET data set and obtained results are compared with other works in this area. The results show improvement in gender classification over existing methods.

[1]  Michael Elad,et al.  Image Sequence Denoising via Sparse and Redundant Representations , 2009, IEEE Transactions on Image Processing.

[2]  M. Elad,et al.  $rm K$-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation , 2006, IEEE Transactions on Signal Processing.

[3]  Ming-Hsuan Yang,et al.  Gender classification with support vector machines , 2000, Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580).

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

[5]  Chengjun Liu,et al.  Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition , 2002, IEEE Trans. Image Process..

[6]  Tieniu Tan,et al.  A Study on Gait-Based Gender Classification , 2009, IEEE Transactions on Image Processing.

[7]  Michael A. Saunders,et al.  Atomic Decomposition by Basis Pursuit , 1998, SIAM J. Sci. Comput..

[8]  Duan-Yu Chen,et al.  Robust gender recognition for real-time surveillance system , 2010, 2010 IEEE International Conference on Multimedia and Expo.

[9]  Rama Chellappa,et al.  Dictionary-Based Face Recognition Under Variable Lighting and Pose , 2012, IEEE Transactions on Information Forensics and Security.

[10]  Shiaofen Fang,et al.  Gender identification using frontal facial images , 2005, 2005 IEEE International Conference on Multimedia and Expo.

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

[12]  David L Donoho,et al.  Compressed sensing , 2006, IEEE Transactions on Information Theory.

[13]  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).

[14]  Anil K. Jain,et al.  Face Detection in Color Images , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[15]  Liming Chen,et al.  Gender identification using a general audio classifier , 2003, 2003 International Conference on Multimedia and Expo. ICME '03. Proceedings (Cat. No.03TH8698).

[16]  Thomas Deselaers,et al.  ClassCut for Unsupervised Class Segmentation , 2010, ECCV.

[17]  Lei Zhang,et al.  Gabor Feature Based Sparse Representation for Face Recognition with Gabor Occlusion Dictionary , 2010, ECCV.

[18]  Bo Wu,et al.  Facial image retrieval based on demographic classification , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[19]  Michael Elad,et al.  Applications of Sparse Representation and Compressive Sensing , 2010, Proc. IEEE.

[20]  Hyeonjoon Moon,et al.  The FERET Evaluation Methodology for Face-Recognition Algorithms , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[21]  Yun Fu,et al.  Gender recognition from body , 2008, ACM Multimedia.

[22]  Allen Y. Yang,et al.  Robust Face Recognition via Sparse Representation , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[23]  Pierre Vandergheynst,et al.  Blind Audiovisual Source Separation Based on Sparse Redundant Representations , 2010, IEEE Transactions on Multimedia.

[24]  Huchuan Lu,et al.  Automatic gender recognition based on pixel-pattern-based texture feature , 2008, Journal of Real-Time Image Processing.