Local structure based sparse representation for face recognition with single sample per person
In this paper, we propose local structure based sparse representation classification (LS SRC) to solve single sample per person (SSPP) problem. By adopting the “divide-conquer-aggregate” strategy, we successfully alleviate the dilemma of high data dimensionality and small samples, where we first divide the face into local blocks, and classify each local block, and then integrate all the classification results by voting. For each block, we further divide it into overlapped patches and assume that these patches lie in a linear subspace. This subspace assumption reflects local structure relationship of the overlapped patches and makes SRC feasible for SSPP problem. To lighten the computing burden, we further propose local structure based collaborative representation classification (LS CRC). Experimental results on three public face databases show that our methods not only generalize well to SSPP problem but also have strong robustness to expression, illumination, little pose variation, occlusion and time variation.