Adaptable Dense Representation with Collaborative Representation Classification for Face Recognition Using Low-Rank Matrices Recovery

Abstract

In recent years, with the great success of sparse representation on face recognition, sparse representation based classification (SRC) and collaborative representation based classification (CRC) are two most popular methods, among which SRC with L1-norm minimization has a relatively better recognition rate but it is time consuming while CRC with L2-norm minimization has a relatively better time efficiency but recognition performance is lower than that of SRC, especially when face images are impaired or undesirable outliers are involved. To achieve both time efficiency as CRC and better recognition performance as SRC, a novel method called adaptable dense representation with CRC (ADR+CRC) is presented, which utilizes ADR for dictionary decomposition using the technique of low-rank matrices recovery to improve the deficiency of CRC that has poor recognition performance when face images are corrupted due to disguise and occlusion. Experimental results show the superiority of the proposed method when compared with the state-of-the-art methods.