Under-sampled Face Recognition via Intra-class Variant Dictionary Modelling

摘要

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 intra-class 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.