Affiliations: [a] School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, Jiangsu, China | [b] Key Laboratory of Advanced Process Control for Light Industry (Jiangnan University), Ministry of Education, Wuxi, Jiangsu, China | [c] School of Computer Science and Engineering, Anhui University of Science & Technology, Huainan, Anhui, China
Correspondence:
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Corresponding author: Hongwei Ge, School of Artificial Intelligence and Computer Science, Jiangnan University, No. 1800, Lihu Avenue, Wuxi, Jiangsu 214122, China. E-mail: ghw8601@163.com.
Abstract: It is an open question to learn an over-complete dictionary from a limited number of face samples, and the inherent attributes of the samples are underutilized. Besides, the recognition performance may be adversely affected by the noise (and outliers), and the strict binary label based linear classifier is not appropriate for face recognition. To solve above problems, we propose a virtual samples based robust block-diagonal dictionary learning for face recognition. In the proposed model, the original samples and virtual samples are combined to solve the small sample size problem, and both the structure constraint and the low rank constraint are exploited to preserve the intrinsic attributes of the samples. In addition, the fidelity term can effectively reduce negative effects of noise (and outliers), and the ε-dragging is utilized to promote the performance of the linear classifier. Finally, extensive experiments are conducted in comparison with many state-of-the-art methods on benchmark face datasets, and experimental results demonstrate the efficacy of the proposed method.
Keywords: Face recognition, dictionary learning, virtual samples, slack linear classifier, structural constraint
