Abstract
When the feature dimension is larger than the number of samples the small sample-size problem occurs. There is great concern about it within the face recognition community. We point out that optimizing the Fisher index in linear discriminant analysis does not necessarily give the best performance for a face recognition system. We propose a new regularization scheme. The proposed method is evaluated using the Olivetti Research Laboratory database, the Yale database, and the Feret database.
Original language | English |
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Pages (from-to) | 1080-1085 |
Number of pages | 6 |
Journal | IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics |
Volume | 37 |
Issue number | 4 |
DOIs | |
Publication status | Published - Aug 2007 |
Scopus Subject Areas
- Control and Systems Engineering
- Software
- Information Systems
- Human-Computer Interaction
- Computer Science Applications
- Electrical and Electronic Engineering
User-Defined Keywords
- Face recognition
- Optimization
- Regularized discriminant analysis (RDA)
- Small sample-size problem