In consideration of the cast shadows

specularities

occlusions and corruptions in the images that violate the low-rank structure

a novel recognition method of joint sparse representation based on low-rank subspace recovery is proposed.Firstly

using all training images of each class to form a data matrix D

we can decompose D as the sum of a low-rank matrix A and a sparse error matrix E

where A denotes thecleanimages which follow strictly the low-rank subspace structure and E accounts for cast shadows

specularities

occlusions and corruptions in the images that violate the low-rank structure.Then the test sample can be represented as the linear combination of dictionary which is composed of low rank matrix and error matrix

using the sparse approximation of this two parts calculates the residual which used for classification.Experiment results show that the algorithm is effective

and effectively improve the recognition accuracy.