Discriminant dictionary learning with sparse embedding on face recognition

Abstract

Sparse dictionary learning on face recognition focuses on representing a face linearly by a set of atoms from the dictionary. How to learn a dictionary is a key issue to sparse representation. Structured dictionary has been used during the process of dictionary learning in order to improve the performance of classification. However, we consider that dictionary should not only be composed of a discriminant dictionary for identity or class information, but also a common dictionary which may contains disturbances and some common features for all class. Meanwhile, most of the proposed methods learns features and dictionary separatively, which may decrease the classification ability. Because projecting the source domain into a low dimensional space before dictionary learning will fail to catch some vital class-specific information which may be learned from dictionary learning. In this paper, a discriminant dictionary learning method with sparse embedding is proposed. Both discriminant and common dictionary are learned under the constraints on pairwise distance of sparsity coefficients, and the projection matrix is learned jointly. Experiments show that our method achieves better performance than other state-of-art methods on face recognition.