Aiming at the problem of human faces wi th varying expression and illumination, as well as occlusion and disguise, a face recognition algorithm is proposed based on local structural sparse representati on. This algorithm combines low rank matrix recovery with structural incoherenc e and discrete cosine transform (DCT) method to remove occlusion, disguise and il lumination variations in face image. Meanwhile, the par tial inf ormation is fully utilized by using sparse codes of local image patches with spatial layout. In th e classification stage, the algorithm effectively improves the recognition rate based on a novel alignment pooling method. Extensive experime nts are conducted on p ublicly available face databases. Compared with the related state of the art met hods, the experimental results demonstrate the accuracy and efficiency of the pr oposed method.