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.
Original language | English |
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Pages (from-to) | 1945-1948 |
Number of pages | 4 |
Journal | Pattern Recognition |
Volume | 37 |
Issue number | 9 |
DOIs | |
State | Published - 1 Sep 2004 |
Keywords
- Discriminant analysis
- Face recognition
- Feature extraction
- Photometric preprocessing
- Principal component analysis
- Regularization
ASJC Scopus subject areas
- Software
- Signal Processing
- Computer Vision and Pattern Recognition
- Artificial Intelligence