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Proceedings Paper

Feature extraction of face images using kernel approach
Author(s): Xiaojun Wu; Jingyu Yang; Shi-Tong Wang; Tong-Ming Liu
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Paper Abstract

Fisher discriminant methods (FDM) have been demonstrated their success in face recognition, detection, and tracking. Fisher discriminant method is based on the optimum of Fisher discriminant criterion. Recently Higher Order Statistics (HOS) has been applied to many pattern recognition problems. In this paper we investigate a generalization of FDM, Kernel Fisher discriminant methods (KFDM), for the feature extraction of face images, which is nonlinear analysis method. In conventional FDM, all the matrices including within -class scatter matrix, between-class scatter matrix and population scatter matrix are actually a second order correlation of patterns respectively, KFDM provides a replacement which takes into account ofhigher order correlation. Further more, KFDM computes the higher order statistics without the combinatorial explosion of time and memory complexity. We compare the recognition results using KFDM with conventional FDM on ORL face image database. Experimental results show that the proposed KFDM outperforms conventional FDM in face recognition.

Paper Details

Date Published: 31 July 2002
PDF: 8 pages
Proc. SPIE 4875, Second International Conference on Image and Graphics, (31 July 2002); doi: 10.1117/12.477059
Show Author Affiliations
Xiaojun Wu, East China Shipbuilding Institute, Nanjing Univ. of S&T, and Shenyang Institute of Automat (China)
Jingyu Yang, Nanjing Univ. of Science and Technology (China)
Shi-Tong Wang, East China Shipbuilding Institute (China)
Tong-Ming Liu, East China Shipbuilding Institute (China)


Published in SPIE Proceedings Vol. 4875:
Second International Conference on Image and Graphics

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