Regularization studies on LDA for face recognition

It is well-known that the applicability of linear discriminant analysis (LDA) to high-dimensional pattern classification tasks such as face recognition (FR) often suffers from the so-called "small sample size" (SSS) problem arising from the small number of available training samples compared to the dimensionality of the sample space. In this paper, we propose a new LDA method that effectively addresses the SSS problem using a regularization technique. In addition, a scheme of expanding the representational capacity of the face database is introduced to overcome the limitation that the LDA based algorithms require at least two samples per class available for learning. Extensive experimentation performed on the FERET database indicates that the proposed methodology outperforms traditional methods such as eigenfaces and direct LDA in a number of SSS setting scenarios.