Extended Locality Preserving Discriminant Analysis for Face Recognition

In this paper, an extended locality preserving discriminant analysis (ELPDA) method is proposed. To address the disadvantages of original locality preserving discriminant analysis (LPDA), a new locality preserving between-class scatter, which is characterized by samples and the corresponding k out-class nearest neighbors, is defined. Moreover, the small sample size problem is also avoided by solving a new optimization function. Experimental results on AR and FERET subsets illustrate the effectiveness of the proposed method for face recognition.