Under-sampled face recognition via intra-class variant dictionary modelling

Many face algorithms require a relatively large number of training samples. In practice, they often face the challenge of inadequate training samples, which reduces their recognition accuracy. Motivated by the extended sparse representation-based classification (ESRC), we propose an improved method to address the problem of under-sampled face recognition. We show that intra-class variant dictionary plays a significant role in feature extraction. Firstly, we propose to use the robust principal component analysis (RPCA) to model the sparse part of face images as intra-class variant dictionary, so that the various changes between faces can be well captured. Secondly, we incorporate the intraclass variant dictionary into the framework of ESRC. Experimental results on the AR and Extended Yale B databases show that our method outperforms other competitors either in the case of cross database recognition or one sample per class.