Cropped and Extended Patch Collaborative Representation Face Recognition for a Single Sample Per Person 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 patchbased method can availably avoid the effect of variations, and the fact that intraclass 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 intraclass 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.

Image Recognition Using Manifold Constrained Collaborative Representation Image recognition is still a challenging task due to the existed illumination and view variations. Manifold learning and representation based classifiers (RCs) are two widely utilized methods to treat the image recognition. The common RCs only emphasize the representation by the training samples globally, while the geometric manifold structure of samples is not fully considered. In this letter, a novel manifold constrained collaborative representation is proposed, which aims to make the representation of query sample be similar with the codes of their nearbypoints. Thus, the obtained representations can be more discriminative for recognition. Extensive experiments on several popular databases show that the our proposed method is promising in recognizing various images.