Sample diversity, representation effectiveness and robust dictionary learning for face recognition

Abstract

Conventional dictionary learning algorithms suffer from the following problems when applied to face recognition. First, since in most face recognition applications there are only a limited number of original training samples, it is difficult to obtain a reliable dictionary with a large number of atoms from these samples. Second, because the face images of the same person vary with facial poses and expressions as well as illumination conditions, it is difficult to obtain a robust dictionary for face recognition. Thus, obtaining a robust and reliable dictionary is a crucial key to improve the performance of dictionary learning algorithms for face recognition. In this paper, we propose a novel dictionary learning framework to achieve this. The proposed algorithm framework takes training sample diversities of the same face image into account and tries to obtain more effective representations of face images and a more robust dictionary. It first produces virtual face images and then designs an elaborate objective function. Based on this objective function, we obtain a mathematically tractable and computationally efficient algorithm to generate a robust dictionary. Experimental results demonstrate that the proposed algorithm framework outperforms some previous state-of-the-art dictionary learning and sparse coding algorithms in face recognition. Moreover, the proposed algorithm framework can also be applied to other pattern classification tasks.