Discriminant Multi-component Face Analysis

Sparse representation based classification (SRC) has attracted much attention in face analysis such as face recognition (FR) and face expression recognition (FER). Currently, most of SRC based methods treated face as a whole component which results in under-utilization of the complementary in different facial parts. In this paper, we present an approach which can effectively explore the complementary of different facial parts to boost the performance of face analysis. In particular, we employ multi-view sparse coding techniques to learn the factorized representation of different facial components. Furthermore, we incorporate label information into the objective function to enforce the discriminability. To evaluate the performance, we conduct face analysis experiments including FR and FER on JAFFE database. Experimental results demonstrate that the proposed method can significantly boost the performance of face analysis.