10. A face recognition system based on a computing device, comprising: a storage module configured to store a plurality of training face images which belongs to a plurality of face classes, wherein a face class includes one or more training face images and represents an identification of the one or more training face images, and to store a plurality of training dictionaries corresponding to the plurality of training face images, wherein the plurality of training dictionaries include a plurality of deep feature matrices; an acquisition module configured to obtain an input face image; a computation module configured to: partition the input face image into a plurality of blocks; extract corresponding deep feature vectors of the plurality of blocks of the input face image using a deep learning network; apply a collaborative representation model to represent the deep feature vectors of the blocks of the input face image with the training dictionaries and representation vectors; compute residual errors for the face classes, a residual error for a face class being a summation of errors for all blocks corresponding to the training face images in the face class, wherein an error for a block exists between a feature vector of the block in the input face image and the collaborative representation model of the block corresponding to the face class; and classify the input face image by selecting a face class that yields a minimum residual error as a recognition face class; and an output module configured to present the recognition face class of the input face image.
11. The face recognition system according to claim 10 , wherein: the training face images are partitioned into a plurality of non-overlapping blocks; a deep feature matrix is extracted from blocks at a same location in the training face images using the deep learning network; and the plurality of blocks of the input face image are non-overlapping.