Regression analysis based methods have shown strong robustness and achieved great
success in face recognition. In these methods, convex-norm and nuclear norm are usually …

In this work, we focus on studying the differentiable relaxations of several linear regression
problems, where the original formulations are usually both nonsmooth with one nonconvex …

In this paper, we consider the robust face recognition problem via iterative re-constrained
group sparse classifier (IRGSC) with adaptive weights learning. Specifically, we propose a …

Most existing regression based classification methods for robust face recognition usually
characterize the representation error using L 1-norm or Frobenius-norm for the pixel-level …

This paper tries to separate fine-grained images by jointly learning the encoding parameters
and codebooks through low-rank sparse coding (LRSC) with general and class-specific …

Dealing with partial occlusion or illumination is one of the most challenging problems in
image representation and classification. In this problem, the characterization of the …

Face identification (FI) via regression-based classification has been extensively studied
during the recent years. Most vector-based methods achieve appealing performance in …