Face Recognition Based on Kernel Discriminative Common Vectors
基于核鉴别共同矢量的小样本脸像鉴别方法

Face recognition tasks always encounter Small Sample Size (SSS) problem, which leads to the ill-posed problem in Fisher Linear Discriminant Analysis (FLDA). The Discriminative Common Vector (DCV) successfully overcomes this problem for FLDA. In this paper, the DCV is extended to nonlinear case, by performing the Gram-Schmidt orthogonalization twice in feature space, which involving computing two kernel matrices and performing a Cholesky decomposition of a kernel matrix. The experimental results demonstrate that the proposed KDCV achieve better performance than the DCV method.