Abstract: A face recognition method based on low-rank recovery sparse representation classifier (LRR_SRC) was proposed to overcome the disadvantages of the face recognition of sparse representation-based classification (SRC),including the poor performance of the unit matrix as the error dictionary in the progress of describing the noise and error of the face images,and the dictionary incompletion caused by the insufficiency of the training samples．Firstly,in this method,training samples are decomposed into a low rank approximation matrix and a sparse error matrix using low-rank recovery (LRR) algorithm．And then,the low-rank approximation matrix and the error matrix compose a dictionary．On the basis of this,the sparse representation of the given test sample can be obtained under this dictionary．Further,using the sparse coefficients associated with the special class,LRR_SRC can approximate the given test sample and calculate the reconstruction error between the given test sample with its approximation associated with the special class．Based on the reconstruction error associated with special class,the given test sample can be classified accurately．Experimental results on face database of YaleB and CMU PIE show that face recognition method proposed in this paper has a higher recognition rate.

Key words: Low rank matrix recovery,Sparse representation,Error dictionary,Face recognition