In this paper, we propose local structure based sparse representation classification (LS
SRC) to solve single sample per person (SSPP) problem. By adopting the “divide-conquer …

Recently, Sparse Representation-based Classification (SRC) has attracted a lot of attention
for its applications to various tasks, especially in biometric techniques such as face …

A limited training set usually limits the performance of face recognition in practice. Even
sparse representation-based methods which outperform in face recognition cannot avoid …

The extended sparse representation classifier (ESRC) is one of the state-of-the-art solutions
for single sample face recognition, but it performs unsatisfactorily under varying illumination …

Sparse representation is an effective data classification algorithm that depends on the
known training samples to categorise the test sample. It has been widely used in various …

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

In real-world face recognition systems, traditional face recognition algorithms often fail in the
case of insufficient training samples. Recently, the face recognition algorithms of sparse …

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

In this paper, we examine the role of feature selection in face recognition from the
perspective of sparse representation. We cast the recognition problem as finding a sparse …

In this paper, a customized sparse representation model is proposed to take advantage of
the variational information for undersampled face recognition. The proposed model with the …