Document Type : Research Paper
Islamic Azad University, Mashhad branch
Computer Engineering Dep. , Islamic Azad University, Mashhad branch
Sparse correntropy model is a face recognition model on the bases of sparse representation which is robust to noise and occlusion. In this mode, a linear combination of training data is determined such that, on the basis correntropy criterion, is as similar as possible to the test data, and L1-norm of coefficient vector of the linear combination is minimum. L1-norm is not differentiable. Therefore, efficient gradient-based methods can not be used to solve the problem. Thus, to simplify the model to be solved fast, the coefficients were considered to be non-negative. The non-negativity constraint is restrictive which can decrease the accuracy of the model. In this paper, to fix this difficulty, L2-norm instead of L1-norm of the linear combination is minimized. Then, a fast algorithm is proposed to solve the novel model. ٍExperimental results confirm that the runtime and accuracy of our proposed method is better than that of sparse correntropy model with non-negative coefficients.