Face recognition based on PCA/KPCA plus CCA

Authors:
Yunhui He;Li Zhao;Cairong Zou
Affiliations:
Department of Radio Engineering, Southeast University, Nanjing, P.R. China;Department of Radio Engineering, Southeast University, Nanjing, P.R. China;Department of Radio Engineering, Southeast University, Nanjing, P.R. China
Venue:
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part II
Year:
2005

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Abstract

Based on the equivalence between canonical correlation analysis (CCA) and Fisher linear discriminant analysis (FLDA), two methods for feature extraction of face images are proposed in this paper. In the first approach, the high-dimensional face images are first mapped into the range space of total scatter matrix using principle component analysis (PCA). Then CCA is performed to extract the linear optimal discriminant features without losing Fisher discriminatory information. In the second approach, nonlinear features are extracted using KPCA+CCA which is equivalent to KFDA in nature. The experimental results upon ORL face database indicate that the proposed PCA/KPCA+CCA significantly outperform the traditional Fisherface method.