By representing the input testing image as a sparse linear combination of the training
samples via l1-norm minimization, sparse representation based classification (SRC) has …

By coding the input testing image as a sparse linear combination of the training samples via
l 1-norm minimization, sparse representation based classification (SRC) has been recently …

Sparse representation based classification (SRC) has recently been proposed for robust
face recognition. To deal with occlusion, SRC introduces an identity matrix as an occlusion …

Sparse representation (or coding)-based classification (SRC) has gained great success in
face recognition in recent years. However, SRC emphasizes the sparsity too much and …

Factors such as misalignment, pose variation, and occlusion make robust face recognition a
difficult problem. It is known that statistical features such as local binary pattern are effective …

In order to overcome the defect that the face recognition (FR) rate is greatly reduced in the
existing uncontrolled environments such as the change of illumination, occlusion, and …

In this paper, a novel joint sparse representation method is proposed for robust face
recognition. We embed both group sparsity and kernelized locality-sensitive constraints into …

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 …

Image feature is a significant factor for the success of robust face recognition. Recently
sparse representation based classifier (SRC) has been widely applied to robust face …

In this paper, we present a sparse correntropy framework for computing robust sparse
representations of face images for recognition. Compared with the state-of-the-art l 1 norm …