关键词: 人脸识别, 块对角结构, 低秩恢复, 子空间投影, 特征提取

Abstract: Due to the self-expressive property of data, the samples from same class have similar representations over a given dictionary, which means the representations of all samples possess block-diagonal structure. But in view of the existence of various corruptions in face images, the sample’s subspace structure may be destroyed. In order to handle this problem, many low-rank representation based methods are proposed to recovery clear components of data, but only low-rank constraint can not transform the original training samples to ideal low-rank subspace perfectly. A Robust Structured Low-Rank Recovery algorithm（RSLRR） is proposed. RSLRR utilizes an ideal label matrix to promotethe low-rank representation tend to a block-diagonal structure which can excavate more potential structural information. Meanwhile, in order to alleviate the structural information loss caused by above strict label pursuing, another relax regularization term are applied to weaken the negative influence of off-block-diagonal coefficients. Afterwards, a discriminative and structured dictionary can be obtained by RSLRR algorithm, and then a low-rank projection matrix is computed to project all test samples into its corresponding low-rank subspaces efficiently. Experimental results on AR and CMU PIE databases demonstrate the effectiveness and robustness of the proposed RSLRR algorithm.

Key words: face recognition, block-diagonal structure, low-rank recovery, subspace projection, feature extraction