Local Structure-Based Sparse Representation for Face Recognition

Abstract

This article presents a simple yet effective face recognition method, called local structure-based sparse representation classification (LS_SRC). Motivated by the “divide-and-conquer” strategy, we first divide the face into local blocks and classify each local block, then integrate all the classification results to make the final decision. To classify each local block, we further divide each block into several overlapped local patches and assume that these local patches lie in a linear subspace. This subspace assumption reflects the local structure relationship of the overlapped patches, making sparse representation-based classification (SRC) feasible even when encountering the single-sample-per-person (SSPP) problem. To lighten the computing burden of LS_SRC, we further propose the local structure-based collaborative representation classification (LS_CRC). Moreover, the performance of LS_SRC and LS_CRC can be further improved by using the confusion matrix of the classifier. Experimental results on four public face databases show that our methods not only generalize well to SSPP problem but also have strong robustness to occlusion; little pose variation; and the variations of expression, illumination, and time.