An efficient face classification method based on shared and class-specific dictionary learning

In this paper, we propose a new dictionary learning method to improve the performance of face recognition by learning a shared dictionary for all persons and a class-specific dictionary for each person. Such shared-dictionary is employed by computing the differences between samples and the centroid of neutral-samples, which improves the representation effort in the case of pose, expression and occlusion changes. Moreover, the discriminative power of learnt class-specific dictionary is further enhanced by enforcing structured dictionaries to be incoherence. Extensive experiments on three databases (AR, CMU-PIE, Extended Yale B) illustrate that the proposed framework achieves dramatic performance on face recognition even when existing big changes between training data and testing samples.