Face recognition based on collaborative image similarity assessment

In this paper, we propose an innovative approach for face recognition based on collaborative image similarity assessment (CISA). In the proposed method, the test sample is first represented by a linear combination of all the training samples for each face class. The classification task is then conducted using the similarity measures including structure similarity index measure (SSIM), root mean square (RMS), and similarity assessment value (SAV). Since CISA is only one phase, it is computationally efficient when comparing with the method of two-phase test sample sparse representation (TPTSR). To verify the performance of face classification, two popular face databases of the ORL and FERET are used. Results show that CISA is comparable with TPTSR on the classification rates for ORL database. However, CISA greatly outperforms TPTSR on the evaluation of the FERET database. Moreover, only 276.4ms on an average is required for CISA in the classification of each test sample but it needs 800.8ms for TPTSR.