Robust face recognition via discriminative and common hybrid dictionary learning

Abstract

Dictionary learning has recently attracted a great deal of attention due to its efficacy in sparse representation based image classification task. There are two main limitations of the Sparse Representation based Classification (SRC) for applications. One is that the training data is required to be less corrupted, and the other is that each class should have sufficient training samples. To overcome these two critical issues, we propose a novel approach, namely Discriminative and Common hybrid Dictionary Learning (DCDL), for solving robust face recognition. With the priori target rank information, the DCDL is able to recover a clean discriminative dictionary by exploiting underlying low-rank structure of training data. Simultaneously, the common intra-class variation dictionary is learned to make sure that a query image can be better represented by the collaboration with image variations of other classes. Extensive experiments on representative face databases show that the proposed approach outperforms the state-of-the-art sparse representation based algorithms in dealing with non-occluded face recognition, and yields significant performance improvements in most cases of occluded face recognition.