16. 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 relaxed collaborative representation problem 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 + λ 2 ⁢ w r ⁢  a r - a _  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 ; ā is a mean vector of all representation vectors a r ; λ 1 >0, λ 2 >0 and y>0 are Lagrange multipliers; λ 2 w r ∥a r |ā∥ 2 2 is a regularizer for a r ; and an entropy term yw r |nw r regularizes the weight w r .
16. 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 relaxed collaborative representation problem 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 + λ 2 ⁢ w r ⁢  a r - a _  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 ; ā is a mean vector of all representation vectors a r ; λ 1 >0, λ 2 >0 and y>0 are Lagrange multipliers; λ 2 w r ∥a r |ā∥ 2 2 is a regularizer for a r ; and an entropy term yw r |nw r regularizes the weight w r . 17. The face recognition system according to claim 16 , wherein the relaxed collaborative representation problem is solved in an alternating fashion between a r and w r , by solving a regularized least squares problem for the coefficients a r having the weights w r and a fixed, and updating w r and ā by fixing a r .