Weighted sparse representation for face recognition

Sparse representation for classification (SRC) has attracted much attention in recent years. In this paper, we improve the typical SRC and propose a new SRC algorithm, i.e., weighted SRC (WSRC). For a test sample, WSRC computes the weight for a training sample according to the distance or similarity relationship between the test sample and the training sample. Then, it represents the test sample by exploiting the weighted training samples based on L1 norm, and classifies the test sample using the representation results. The goal of WSRC is that given a test sample, WSRC pays more attention to those training samples that are more similar to the test sample in representing the test sample. In general, the representation result of WSRC is sparser than that of SRC, and can obtain the better recognition results. The experiments on four popular face data sets show that the proposed algorithm can achieve desirable recognition performance.