Doubly weighted nonnegative matrix factorization for imbalanced face recognition

We propose in this paper a novel doubly weighted nonnegative matrix factorization (DWNMF) method for imbalanced face recognition. Motivated by the fact that some face samples and certain parts of each face sample are more useful for recognition, we construct two weighted matrices based on the pairwise similarity of face samples in the same class and the discriminant score of each face pixel. Compared with the existing NMF algorithm, the proposed DWNMF method can more effectively exploit the discriminative and geometrical information of face samples, and it is especially suitable for imbalanced face recognition. Experimental results are presented to demonstrate the efficacy of the proposed method.