Nonsingular Discriminant Feature Extraction for Face Recognition

Abstract

In this paper, we present a nonsingular transformation prior to performing Fisher linear discriminant analysis (LDA). This method is used to transform general features using all eigenvectors of the scatter matrix with nonzero eigenvalues. As a result, the scatter matrix of transformed features is nonsingular. Subsequently, the discriminant transformation is applied according to LDA using the new scatter matrices. The superiority of nonsingular discriminant analysis of the between-class matrix comes from the shrinkage of within-class scatters and accordingly the enhancement of Fisher class separability. From experiments on facial databases, we find that the nonsingular discriminant feature extraction achieves significant face recognition performance compared to other LDA-related methods for a wide range of sample sizes and class numbers.