Kernel discriminative common vectors for face recognition

Abstract

In face recognition tasks, the existing methods to deal with small sample size problem in linear discriminant analysis (LDA) have their respective drawbacks. However, the recently proposed discriminative common vectors (DCV) method successfully overcomes these drawbacks with high performance in terms of accuracy, real-time performance, and numerical stability. In this paper, the second algorithm in the DCV based on the Gram-Schmidt orthogonalization is extended to the nonlinear case by using kernel method. The Gram-Schmidt orthogonalization procedure in feature space is first presented. Then the algorithm for KDCV is developed which involves performing the Gram-Schmidt orthogonalization procedure twice in feature space. Experiments on ORL database indicate that the proposed KDCV method achieves higher recognition rate than the DCV method.