Cropped and Extended Patch Collaborative Representation Face Recognition for a Single Sample Per Person

Abstract

Face recognition for a single sample per person (SSPP) is a challenging task due to the lack of sufficient sample information. In this paper, in order to raise the performance of face recognition for SSPP, we propose an algorithm of cropped and extended patch collaborative representation for a single sample per person (CEPCRC). Considering the fact that patch-based method can availably avoid the effect of variations, and the fact that intra-class variations learned from a generic training set can sparsely represent the possible facial variations, thus, we extend patch collaborative representation based classification into the SSPP scenarios by using the intra-class variant dictionary and learn patch weight by exploiting regularized margin distribution optimization. For more complementary information, we construct multiple training samples by the means of cropping. Experimental results in the CMU PIE, Extended Yale B, AR, and LFW datasets demonstrate that CEPCRC performs better compared to the related algorithms.