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.
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. 18. The face recognition system according to claim 10 , wherein: provided that T denotes the training face images, y denotes the input face image, the training face images and the test face image are partitioned into p blocks, F(•) denotes deep features extracted from blocks in training face images, F(T r ) denotes the deep feature matrix of a r th block in the training face images, f(y r ) denotes the deep feature vector of the r th block from the input face image, a r denotes a representation vector of the r th block, and a* r denotes a resultant representation vector of the r th block, the representation vector is obtained by solving a collaborative representation problem with regularized least square a * r , ⁢ w * r = arg ⁢ ⁢ min a r , w r ⁢ ∑ r = 1 p ⁢  f ⁡ ( y r ) - F ⁡ ( T r ) ⁢ a r  2 2 + λ 1 ⁢  a r  2 2 + ⁢ + ⁢ γ ⁢ ⁢ w r ⁢ ln ⁢ ⁢ w r wherein: a weight w r is assigned to each representation vector a r , and w* r corresponds to the representation vector a* r ; λ 1 >0 and y>0 are Lagrange multipliers; and an entropy term yw r |nw r regularizes the weight w r .