Local structure based multi-phase collaborative representation for face recognition with single sample per person

Many real-world face recognition applications can only provide single sample for each person, while most face recognition approaches require a large set of training samples, which leads to single sample per person (SSPP) problem. In this paper, we propose local structure based multi-phase collaborative representation classification (LS_MPCRC) to solve 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 local block, we further divide it into overlapped local patches and assume that these patches lie in a linear subspace. This subspace assumption reflects local structure relationship of the overlapped patches and makes CRC robust for SSPP problem. Motivated by the fact that the entropy of the class probability distribution is a measure about classification confidence, we further apply multi-phase technique to reduce entropy, where useless classes are eliminated after each phase classification. This strategy finally produces a sparse class probability distribution with higher classification confidence. Experimental results show that the proposed method generalizes well to SSPP problem and outperforms many state-of-the-art methods. It also shows strong robustness to the large variation of expression, illumination, little poses variation, occlusion and time variation.