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Regularized discriminant analysis for face recognition
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DOI：10.1016/j.patcog.2004.03.011.png)
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Abstract
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This paper studies regularized discriminant analysis (RDA) in the context of face recognition. We check RDA sensitivity to different photometric preprocessing methods and compare its performance to other classifiers. Our study shows that RDA is better able to extract the relevant discriminatory information from training data than the other classifiers tested, thus obtaining a lower error rate. Moreover, RDA is robust under various lighting conditions while the other classifiers perform badly when no photometric method is applied. (C) 2004 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.
Keywords:
face recognition
feature extraction
regularization
principal component analysis
discriminant analysis
photometric preprocessing
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