关键词: 人脸识别, 稀疏表示, 低秩矩阵恢复, 坐标下降法, 基于稀疏表示的分类（SRC）算法

Abstract: The main difficulty in terms of face recognition lies in the occlusion as well as the changes in lightning and expre-
ssions, both of which may result in the similarity between the different face images. The classification algorithm based on sparse representation（SRC） is a classical face recognition algorithm. However, such method has the problem that the recognition rate decreases and the sparse representation has low efficiency when face training samples are insufficient. To eliminate these drawbacks, this paper proposes a face recognition algorithm based on discriminative Low Rank Recovery Fast Sparse Representation-based Classification（LRR_FSRC）. Firstly, low-rank decomposition theory is used to obtain a low-rank recovery dictionary and a sparse error dictionary. This is followed by the integrated utilization of the low-rank decomposition and structural incoherence theory, which are used to train discriminative low-rank dictionary and sparse error dictionary. The discriminative low-rank dictionary and sparse error dictionary are combined as dictionary for testing. Secondly, the method of coordinate descent is used to figure out the sparse coefficient to improve the computational efficiency. Finally, according to the reconstruction error, the classification of the test sample is achieved. Experimental results on the YALE and ORL databases show that the LRR_FSRC based face recognition method proposed in this paper has higher recognition rate and faster computational efficiency.

Key words: face recognition, sparse representation, low rank matrix recovery, coordinate descent method, Sparse Representation-based Classification（SRC） method