Optimized Coefficient Vector and Sparse Representation-Based Classification Method for Face Recognition

Sparse representation based classification method has led to a wide variety of extensions of representation based methods for face recognition. All of these methods partially reveal that collaborative representation is a crucial factor to make sparse representation based classification powerful for face recognition. The collaborative representation based classification (CRC) methods and corresponding variations have achieved effective results in face recognition. For these methods, we found that the test sample has some relevance with the coefficient vector. For example, nonzero elements in the coefficient vector are associated with the classes which the test sample potentially belong to. Exploiting the relevance may obtain sparser coefficient vector in comparison with the traditional methods. Hence, we propose a novel method in which the test sample is closely involved in the solution procedure of optimal coefficient vector. The classification of the proposed method is performed by checking the minimal residual between the test sample and the collaborative representation with respect the test sample of the selected class, which is similar to that of CRC. The proposed method can intensify the corresponding coefficients in the coefficient vector by exploiting the test sample. Experimental results show that the proposed method does achieve more accurate recognition rate.