A New Weighted Sparse Representation Based on MSLBP and Its Application to Face Recognition

Face recognition via sparse representation-based classification has received more and more attention in recent years. This approach has achieved state-of-the-art results, which outperforms traditional methods, especially when face image pixels are corrupted or occluded. In this paper, we propose a new weighted sparse representation method called WSRC-MSLBP which utilizes the multi-scale LBP (MSLBP) feature to measure similarity between face images, and to form the weight matrix. The proposed WSRC-MSLBP method not only represents the test sample as a sparse linear combination of all the training samples, but also makes use of locality of local binary pattern. Experimental results on publicly available databases show that the proposed WSRC-MSLBP method is more effective than sparse representation-based classification algorithm and the original weighted sparse representation method.