Modular Weighted Global Sparse Representation for Robust Face Recognition

Abstract

This work proposes a novel framework of robust face recognition based on the sparse representation. Image is first divided into modules and each module is processed separately to determine its reliability. A reconstructed image from the modules weighted by their reliability is formed for the robust recognition. We propose to use the modular sparsity and residual jointly to determine the modular reliability. The proposed framework advances both the modular and global sparse representation approaches, especially in dealing with disguise, large illumination variations and expression changes. Compared with the related state-of-the-art methods, experimental results on benchmark face databases verify the advancement of the proposed method.