Regularization studies of linear discriminant analysis in small sample size scenarios with application to face recognition

It is well-known that the applicability of linear discriminant analysis (LDA) to high-dimensional pattern classification tasks such as face recognition 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 attempts to address the SSS problem using a regularized Fisher's separability criterion. In addition, a scheme of expanding the representational capacity of face database is introduced to overcome the limitation that the LDA-based algorithms require at least two samples per class available for learning. Extensive experiments performed on the FERET database indicate that the proposed methodology outperforms traditional methods such as Eigenfaces and some recently introduced LDA variants in a number of SSS scenarios.