Face recognition based on kernel discriminative common vectors

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 p...

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Published inDian zi yu xin xi xue bao = Journal of electronics & information technology Vol. 28; no. 12; pp. 2296 - 2300
Main Authors He, Yun-Hui, Zhao, Li, Zou, Cai-Rong
Format Journal Article
LanguageChinese
Published 01.12.2006
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Summary: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.
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ISSN:1009-5896