Optimized regularized linear discriminant analysis for feature extraction in face recognition

Abstract

In a reduced dimensional space, linear discriminant analysis looks for a projective transformation that can maximizes separability among classes. Since linear discriminant analysis demands the within-class scatter matrix appear to non-singular, which cannot directly used in condition of small sample size (SSS) issues in which the dimension of image is much higher, while the number of samples isn’t unlimited. Both the between-class and within-class scatter matrices are always exceedingly ill-posed in SSS problems. And many algorithms are suffered from small sample size issues still. To solve SSS problems, many methods including regularized linear discriminant analysis were proposed. In this article, a way was presented by optimized regularized linear discriminant analysis for feature extraction in FR which can not only fix the singularity problem existing in scatter matrix but also the problem of parameter estimation. The experiment is conducted on several databases and promising results are obtained compared to some state-of-the-art methods to demonstrate the effectiveness of the proposed approach.