Do singular values contain adequate information for face recognition?
Singular values (SVs) have been used for face recognition by many researchers. In this paper, we show that the SVs contain little useful information for face recognition and most important information is encoded in the two orthogonal matrices of the SVD. Experimental results are given to support this observation. To overcome this problem, a new method for face recognition based on the above finding is proposed. The face image is projected on to the orthogonal basis of SVD and then the vectors of coefficients are used as the face image features. By using probability density of this image feature obtained by a simplified EM algorithm, the Bayesian classifier is adopted to recognize the unknown faces. The proposed algorithm obtains acceptable experimental results on the ORL face database. (C) 2002 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.
face recognition svd orthogonal decomposition bayesian decision