A two-phase representation based face recognition method with ‘random-filtering’ virtual samples

Collaborative representation classification (CRC) has attracted increasing attention in face recognition (FR) tasks. The two-phase sparse representation (TPSR) methods are the improved schemes. However, most TPSR methods decrease training samples in the first step, resulting in less similarities or discrimination for representation, even unstable classification. In this paper, we propose a new two-phase representation based FR approach with random-filtering virtual samples, called Random-Filtering based Sparse Representation (RFSR) scheme. To increase the similarity in the same class and the discrimination between different classes, RFSR first uses original training samples and their corresponding random-filtering virtual samples to constructs a new training set. Then it exploits the new training set to perform CRC. The experiment results indicate that our method outperforms the two-phase test sample sparse representation (TPTSSR) method and the simple and fast representation-based (SFRB) scheme.