Title: Kernel Canonical Correlation Analysis and Application for Face Discrimination
Abstract: Based on the equivalence between canonical correlation analysis(CCA) and Fisher linear discriminant analysis(FLDA),nonlinear discriminant features of face images are extracted with kernel CCA.These features are equivalent to those extracted with KFDA.Experimental results demonstrate that KCCA is similar to GDA and significantly better than FLDA.
Publication Year: 2006
Publication Date: 2006-01-01
Language: en
Type: article
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