Learning discriminative singular value decomposition representation for face recognition

Abstract

Face representation is a critical step in face recognition. Recently, singular value decomposition (SVD) based representation methods have attracted researchers׳ attentions for their power of alleviating the facial variations. The SVD representation reveals that the SVD basis set is important for the recognition purpose and the corresponding singular values (SVs) are regulated to form a more effective representation image. However, there exists a common problem in the existing SVD based representation methods: they all empirically make a rule to regulate the SVs, which is obviously not optimal in theory. To address this problem, in this paper, we propose a novel method named learning discriminative singular value decomposition representation (LDSVDR) for face recognition. We build an individual SVD basis set for each image and then learn a common set of SVs by taking account of the information in the basis sets according to a discriminant criterion across the training images. The proposed model is solved by sequential quadratic programming (SQP) method. Extensive experiments are conducted on three popular face databases and the results demonstrate the effectiveness of our method when dealing with variations of illumination, occlusion, disguise and face sketch recognition task.