Abstract: In the field of face recognition, when the training samples and test samples are subject to severe noise pollution, the performance of the traditional subspace learning and the classical sparse re-presentation (SRC) will drop sharply. In addition, the method based on sparse representation also faces the problem of computational complexity. In order to alleviate those problems, an occlusion face recognition method based on discriminating low-rank matrix recovery and collaborative representation is proposed. Firstly, the low-rank matrix recovery can recover the clean training samples with low-rank structure from the contaminated training samples and the structural non-correlation constraints can improve the discriminating ability of the recovered data effectively. Secondly, by learning the low-rank projection matrix between the original contaminated data and the recovered low-rank data, the contaminated test samples are projected into the corresponding low dimensional subspace to perform its correction. Finally, the modified test samples are classified by the collaborative representation classification method (CRC) to obtain the final recognition result. Experimental results on Extended Yale B and AR databases show that the proposed method has better recognition performance in occlusion face recognition.

Key words: face recognition, discriminant low-rank matrix recovery, low-rank projection matrix, collaborative representation ,