Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Kernel Eigenfaces vs. Kernel Fisherfaces: Face Recognition Using Kernel Methods
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Feature extraction via generalized uncorrelated linear discriminant analysis
ICML '04 Proceedings of the twenty-first international conference on Machine learning
SIAM Journal on Matrix Analysis and Applications
Foley-Sammon optimal discriminant vectors using kernel approach
IEEE Transactions on Neural Networks
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Feature extraction is one of the most important problems in face recognition task. In this paper, we use kernel uncorrelated discriminant analysis to extract the optimal discriminant features for face recognition. The method also solves the so-called “Small Sample Size” (SSS) problem, which exists in most Face Recognition tasks. Experimental results on the Yale face database and AT&T face database show the effectiveness of this method.