Regularized kernel locality preserving discriminant analysis for face recognition

Abstract

In this paper, a regularized kernel locality preserving discriminant analysis (RKLPDA) method is proposed for facial feature extraction and recognition. The proposed RKLPDA comes into the characteristic of LPDA that encodes both the geometrical and discriminant structure of the data manifold, and improves the classification ability for linear non-separable data by introducing kernel trick. Meanwhile, by regularizing the eigenvectors of the kernel locality preserving within-class scatter, RKLPDA utilizes all the discriminant information and eliminates the small sample size (SSS) problem. Experiments on ORL and FERET face databases are performed to test and evaluate the proposed algorithm. The results demonstrate the effectiveness of RKLPDA. © 2010 Springer-Verlag.