Face recognition based on nonsubsampled contourlet transform and block-based kernel Fisher linear discriminant

Face representation, including both feature extraction and feature selection, is the key issue for a successful face recognition system. In this paper, we propose a novel face representation scheme based on nonsubsampled contourlet transform (NSCT) and block-based kernel Fisher linear discriminant (BKFLD). NSCT is a newly developed multiresolution analysis tool and has the ability to extract both intrinsic geometrical structure and directional information in images, which implies its discriminative potential for effective feature extraction of face images. By encoding the the NSCT coefficient images with the local binary pattern (LBP) operator, we could obtain a robust feature set. Furthermore, kernel Fisher linear discriminant is introduced to select the most discriminative feature sets, and the block-based scheme is incorporated to address the small sample size problem. Face recognition experiments on FERET database demonstrate the effectiveness of our proposed approach.