Paper
31 July 2002 Feature extraction of face images using kernel approach
Xiaojun Wu, Jingyu Yang, Shi-Tong Wang, Tong-Ming Liu
Author Affiliations +
Proceedings Volume 4875, Second International Conference on Image and Graphics; (2002) https://doi.org/10.1117/12.477059
Event: Second International Conference on Image and Graphics, 2002, Hefei, China
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.
© (2002) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Xiaojun Wu, Jingyu Yang, Shi-Tong Wang, and Tong-Ming Liu "Feature extraction of face images using kernel approach", Proc. SPIE 4875, Second International Conference on Image and Graphics, (31 July 2002); https://doi.org/10.1117/12.477059
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KEYWORDS
Feature extraction

Facial recognition systems

Fused deposition modeling

Databases

Pattern recognition

Visualization

Detection and tracking algorithms

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