Face recognition technique has obtained great progress and excellent results on public data
sets. However, traditional algorithms suffer from various changes such as illumination …

We merge illumination normalization and component features into the framework of Sparse
Representation-based Classification (SRC) for face recognition across illumination. Unlike …

Even though face recognition is one of the most studied pattern recognition problems, most
existing solutions still lack efficiency and high speed. Here, the authors present a new …

This paper addresses the problem of face recognition under pose and illumination
variations, and proposes a novel algorithm inspired by the idea of sparse representation …

Single-sample face recognition is one of the most challenging problems in face recognition.
We propose a novel face recognition algorithm to address this problem based on a sparse …

Under uneven illumination, the performances degrade significantly for some existing face
recognition methods. It is a challenge for face recognition methods to work effectively under …

Face recognition (FR) via sparse representation has been widely studied in the past several
years. Recently many sparse representation based face recognition methods with …

Sparse representation based face recognition (SRC) has been paid much attention in recent
years. Representative algorithms are deformable sparse-representation based classification …

This paper focuses on enhancing Sparse Representation based Classifier (SRC) in single-
sample face recognition tasks under varying illumination conditions. The major contribution …

This article presents a simple yet effective face recognition method, called local structure-
based sparse representation classification (LS_SRC). Motivated by the “divide-and …