Sparse Representation Based Discriminative Canonical Correlation Analysis for Face Recognition

Abstract

Canonical correlation analysis (CCA) has been widely used in pattern recognition and machine learning. However, both CCA and its extensions sometimes cannot give satisfactory results. In this paper, we propose a new CCA-type method termed sparse representation based discriminative CCA (SPDCCA) by incorporating sparse representation and discriminative information simultaneously into traditional CCA. In particular, SPDCCA not only preserves the sparse reconstruction relationship within data based on sparse representation, but also preserves the maximum-margin based discriminative information, and thus it further enhances the classification performance. Experimental results on Yale, Extended Yale B, and ORL datasets show that SPDCCA outperforms both CCA and its extensions including KCCA, LPCCA and LDCCA in face recognition.