Regularized discriminant analysis for face recognition |
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Authors: | Itzik Pima |
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Affiliation: | Department of Electrical and Computer Engineering, Ben-Gurion University of the Negev P.O. Box 653, Beer-Sheva 84105, Israel |
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Abstract: | 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. |
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Keywords: | Face recognition Feature extraction Regularization Principal component analysis Discriminant analysis Photometric preprocessing |
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