Face Verification With Local Sparse Representation

Chih-Hsueh Duan, Chen-Kuo Chiang, Shang-Hong Lai

Abstract: In this letter, a local sparse representation is proposed for face components to describe the local structure and characteristics of the face image for face verification. We first learn a dictionary from collected local patches of face images. Then, a novel local descriptor is presented by using sparse coefficients obtained by the learned dictionary and local face patches from face components to represent the entire human face. We demonstrate the performance of the proposed local sparse representation method on several publicly available datasets. Extensive experiments on both CMU PIE dataset and the challenging LFW database have shown the effectiveness of the proposed method.