Abstract
The linear (Fisher) discriminant analysis (LDA) is a well-known and popular statistical method in pattern recognition and classification. When applied to face recognition problem the small sample size problem occurs. We investigate the nature of this phenomenon and use wavelet transform for dimension reduction. Moreover we propose a regularized scheme based face recognition system. Comparisons are made with the Tikhonov regularization method and the infinity Fisher index method. We find out that when the small sample size problem appears optimizing the Fisher index does not lead to good results.
Original language | English |
---|---|
Pages (from-to) | 307-318 |
Number of pages | 12 |
Journal | Applied Mathematics and Computation |
Volume | 175 |
Issue number | 1 |
DOIs | |
Publication status | Published - 1 Apr 2006 |
Scopus Subject Areas
- Computational Mathematics
- Applied Mathematics
User-Defined Keywords
- Face recognition
- Linear discriminant analysis
- Regularization
- Singular value decomposition
- Small sample size problem
- Wavelet transform