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Facial Gender Recognition via Low-rank and Collaborative Representation in An Unconstrained Environment

  • Sun, Ning (Engineering Research Center of Wideband Wireless Communication Technology, Ministry of Education, Nanjing University of Posts and Telecommunications) ;
  • Guo, Hang (Engineering Research Center of Wideband Wireless Communication Technology, Ministry of Education, Nanjing University of Posts and Telecommunications) ;
  • Liu, Jixin (Engineering Research Center of Wideband Wireless Communication Technology, Ministry of Education, Nanjing University of Posts and Telecommunications) ;
  • Han, Guang (Engineering Research Center of Wideband Wireless Communication Technology, Ministry of Education, Nanjing University of Posts and Telecommunications)
  • Received : 2017.01.04
  • Accepted : 2017.05.25
  • Published : 2017.09.30

Abstract

Most available methods of facial gender recognition work well under a constrained situation, but the performances of these methods have decreased significantly when they are implemented under unconstrained environments. In this paper, a method via low-rank and collaborative representation is proposed for facial gender recognition in the wild. Firstly, the low-rank decomposition is applied to the face image to minimize the negative effect caused by various corruptions and dynamical illuminations in an unconstrained environment. And, we employ the collaborative representation to be as the classifier, which using the much weaker $l_2-norm$ sparsity constraint to achieve similar classification results but with significantly lower complexity. The proposed method combines the low-rank and collaborative representation to an organic whole to solve the task of facial gender recognition under unconstrained environments. Extensive experiments on three benchmarks including AR, CAS-PERL and YouTube are conducted to show the effectiveness of the proposed method. Compared with several state-of-the-art algorithms, our method has overwhelming superiority in the aspects of accuracy and robustness.

Keywords

References

  1. Golomb, Beatrice A., David T. Lawrence, and Terrence J. Sejnowski, "Sexnet: a neural network identifies sex from human faces," NIPS, Vol.1, 1990.
  2. Moghaddam, Baback, and Ming-Hsuan Yang, "Learning gender with support faces," IEEE Transactions on Pattern Analysis and Machine Intelligence24.5, 707-711, 2002. https://doi.org/10.1109/34.1000244
  3. Baluja, Shumeet, and Henry A. Rowley, "Boosting sex identification performance," International Journal of computer vision 71.1, 111-119, 2007. https://doi.org/10.1007/s11263-006-8910-9
  4. Buchala, Samarasena, et al., "Principal component analysis of gender, ethnicity, age, and identity of face images," in Proc. of IEEE ICMI 7, 2005.
  5. Bekios-Calfa, Juan, Jose M. Buenaposada, and Luis Baumela, "Revisiting linear discriminant techniques in gender recognition," IEEE Transactions on Pattern Analysis and Machine Intelligence 33.4, 858-864, 2011. https://doi.org/10.1109/TPAMI.2010.208
  6. Jain, Amit, Jeffrey Huang, and Shiaofen Fang, "Gender identification using frontal facial images," in Proc. of Multimedia and Expo, 2005. ICME 2005. IEEE International Conference on. IEEE, 2005.
  7. Sun N, Zheng W, Sun C, et al., "Gender classification based on boosting local binary pattern," in Proc. of International Symposium on Neural Networks. Springer Berlin Heidelberg, 2006.
  8. Alexandre, Luis A., "Gender recognition: A multiscale decision fusion approach," Pattern Recognition Letters 31.11, 1422-1427, 2010. https://doi.org/10.1016/j.patrec.2010.02.010
  9. Wang, Jian-Gang, et al., "Boosting dense SIFT descriptors and shape contexts of face images for gender recognition," in Proc. of Computer Vision and Pattern Recognition Workshops (CVPRW), 2010 IEEE Computer Society Conference on. IEEE, 2010.
  10. Fellous, Jean-Marc, "Gender discrimination and prediction on the basis of facial metric information," Vision research 37.14, 1961-1973, 1997. https://doi.org/10.1016/S0042-6989(97)00010-2
  11. Saatci, Yunus, and Christopher Town, "Cascaded classification of gender and facial expression using active appearance models," Automatic Face and Gesture Recognition, 2006. FGR 2006. 7th International Conference on. IEEE, 2006.
  12. Cao, Deng, et al., "Can facial metrology predict gender?," in Proc. of Biometrics (IJCB), 2011 International Joint Conference on. IEEE, 2011.
  13. Ng, Choon-Boon, Yong-Haur Tay, and Bok-Min Goi, "A convolutional neural network for pedestrian gender recognition," in Proc. of International Symposium on Neural Networks. Springer Berlin Heidelberg, 2013.
  14. Levi, Gil, and Tal Hassner, "Age and gender classification using convolutional neural networks," in Proc. of the IEEE Conference on Computer Vision and Pattern Recognition Workshops. 2015.
  15. Kalansuriya, Thakshila R., and Anuja T. Dharmaratne, "Neural network based age and gender classification for facial images," ICTer 7.2, 2014.
  16. Liew, Shan Sung, et al., "Gender classification: a convolutional neural network approach," Turkish Journal of Electrical Engineering & Computer Sciences 24.3, 1248-1264, 2016. https://doi.org/10.3906/elk-1311-58
  17. Zhang, Hao, Qing Zhu, and Xiaoqi Jia, "An Effective Method for Gender Classification with Convolutional Neural Networks," in Proc. of International Conference on Algorithms and Architectures for Parallel Processing. Springer International Publishing, 2015.
  18. Zhang, Lei, et al., "Collaborative representation based classification for face recognition," arXiv preprint arXiv:1204.2358, 2012.
  19. Peng, Yigang, et al., "RASL: Robust alignment by sparse and low-rank decomposition for linearly correlated images," IEEE Transactions on Pattern Analysis and Machine Intelligence 34.11 2233-2246, 2012. https://doi.org/10.1109/TPAMI.2011.282
  20. Zhang, Lei, Meng Yang, and Xiangchu Feng., "Sparse representation or collaborative representation: Which helps face recognition?," in Proc. of Computer vision (ICCV), 2011 IEEE international conference on. IEEE, 2011.
  21. Martinez, Aleix M., "The AR face database," CVC technical report 24, 1998.
  22. Gao, Wen, et al., "The CAS-PEAL large-scale Chinese face database and baseline evaluations," IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans 38.1, 149-161, 2008.
  23. Wolf, Lior, Tal Hassner, and Itay Maoz., "Face recognition in unconstrained videos with matched background similarity," in Proc. of Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on. IEEE, 2011.
  24. Kim, Seung-Jean, et al., "An Interior-Point Method for Large-Scale l1-Regularized Least Squares," IEEE journal of selected topics in signal processing 1.4, 606-617, 2007. https://doi.org/10.1109/JSTSP.2007.910971
  25. Wright, Stephen J., Robert D. Nowak, and Mario AT Figueiredo., "Sparse reconstruction by separable approximation," IEEE Transactions on Signal Processing 57.7, 2479-2493, 2009. https://doi.org/10.1109/TSP.2009.2016892
  26. Hinton, Geoffrey E., and Ruslan R. Salakhutdinov., "Reducing the dimensionality of data with neural networks," science 313.5786, 504-507, 2006. https://doi.org/10.1126/science.1127647