This paper addresses the problem of face recognition when there is only few, or even only a
single, labeled examples of the face that we wish to recognize. Moreover, these examples …

Sparse Representation-Based Classification (SRC) is a face recognition breakthrough in
recent years which has successfully addressed the recognition problem with sufficient …

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

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 …

Face recognition (FR) with a single training sample per person (STSPP) is a very
challenging problem due to the lack of information to predict the variations in the query …

Sparse representation provides an effective tool for classification under the conditions that
every class has sufficient representative training samples and the training data are …

In this paper, we address the problem of robust face recognition with undersampled training
data. Given only one or few training images available per subject, we present a novel …

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 …

The success of sparse representation based classification (SRC) has largely boosted the
research of sparsity based face recognition in recent years. A prevailing view is that the …

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 …