Local and Non Local Feature Based Kernel Nonnegative Matrix Factorization Method for Face Recognition
Based on the kernel method and graph theory, this paper proposes a novel Kernel Non negative Matrix Factorization with Local and Non local feature (LN KNMF) approach for face recognition. We establish the objective function in kernel space which incorporates two scatter quantities, namely local scatter and non local scatter. They are determined by the local adjacent graph matrix and non local adjacent graph matrix respectively. The update rules of the proposed LN KNMF method are derived using polynomial kernel function and gradient descent method. Subsequently, we theoretically prove the convergence of the proposed algorithm by means of a constructed auxiliary function. Experimental results on the ORL face database demonstrate the superior performance of our approach against some NMF based methods.
2016 12th International Conference on Computational Intelligence and Security (CIS)