Robust sparse representation has shown significant potential in solving challenging
problems in computer vision such as biometrics and visual surveillance. Although several …

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 …

Sparse representation has shown an attractive performance in a number of applications.
However, the available sparse representation methods still suffer from some problems, and …

We consider the problem of automatically recognizing human faces from frontal views with
varying expression and illumination, as well as occlusion and disguise. We cast the …

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 …

Image classification is a fundamental component in modern computer vision systems, where
sparse representation-based classification has drawn a lot of attention due to its robustness …

In many problems in computer vision, data in multiple classes lie in multiple low-dimensional
subspaces of a high-dimensional ambient space. However, most of the existing …

l 1-minimization refers to finding the minimum l 1-norm solution to an underdetermined
linear system\mbi b= A\mbi x. Under certain conditions as described in compressive sensing …

This paper proposes a novel nonnegative sparse representation approach, called two-stage
sparse representation (TSR), for robust face recognition on a large-scale database. Based …

Sparse representation based classification (SRC) methods have recently drawn much
attention in face recognition, due to their good performance and robustness against …