• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

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

Vol. 11, No.9, September 30, 2017
10.3837/tiis.2017.09.018, Download Paper (Free):

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 l2-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.


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Cite this article

[IEEE Style]
Ning Sun, Hang Guo, Jixin Liu and Guang Han, "Facial Gender Recognition via Low-rank and Collaborative Representation in An Unconstrained Environment," KSII Transactions on Internet and Information Systems, vol. 11, no. 9, pp. 4510-4526, 2017. DOI: 10.3837/tiis.2017.09.018

[ACM Style]
Sun, N., Guo, H., Liu, J., and Han, G. 2017. Facial Gender Recognition via Low-rank and Collaborative Representation in An Unconstrained Environment. KSII Transactions on Internet and Information Systems, 11, 9, (2017), 4510-4526. DOI: 10.3837/tiis.2017.09.018