Normalized Direct Linear Discriminant Analysis with its Application to Face Recognition

Abstract

The dimensionality of sample is often larger than the number of training samples for highdimensional pattern recognition such as face recognition. Here linear discriminant analysis (LDA) cannot be performed directly because of the singularity of the within-class scatter matrix. This is socalled “small sample size” (SSS) problem. PCA plus LDA (FDA) and Direct LDA (DLDA) are two popular methods to solve the SSS problem of LDA. In this paper, we point out the relationship of these two methods and discuss the deficiency of DLDA. Then a normalized direct linear discriminant analysis (NDLDA) method which overcomes DLDA’s deficiency is proposed. Experiments on ORL, YALE and AR face databases show NDLDA’s superiority over DLDA and FDA.