Dominant singular value decomposition representation for face recognition

We propose in this paper a new dominant singular value decomposition representation (DSVDR) method for face recognition. Motivated by the fact that each grayscale face image can be decomposed into a composition of a set of bases by the well-known singular value decomposition (SVD) technique and each basis contains different discriminative and reconstructive information for face representation, we present a new face representation method to select a subset of important bases and regulate their singular values (SVs) according to their discriminative and reconstructive power simultaneously for face recognition. Experimental results demonstrate the efficacy of the proposed method. (C) 2009 Elsevier B.V. All rights reserved.