For the high computational complexity of sparse description which results in the difficulty in meeting the actual needs and the number of samples sensitive training, we proposed a differences Shearlet characteristic of fast sparse description method to describe face recognition. First, Shearlet was used to get multi-scale and multi-direction facial image. Then a matching score fusion strategy was used to integrate Shearlet characteristics, and discriminative characteristics were constituted. Furthermore, configured the "best" sparse description for each test sample and calculated the correlated coefficient. Finally, according to the contribution size of the training sample in a test sample description, achieved the test sample image classification. Experimental results on ORL and YALE face database show that the algorithm ensuring a high recognition rate advantage as well as significantly reducing the time complexity.