In the past few years, deep learning-based models have been very successful in achieving
state-of-the-art results in many tasks in computer vision, speech recognition, and natural …

Sparse representation has attracted much attention from researchers in fields of signal
processing, image processing, computer vision, and pattern recognition. Sparse …

Feature extraction is vital for face recognition. In this paper, we focus on the general feature
extraction framework for robust face recognition. We collect about 300 papers regarding face …

Recent evidence in mental health assessment have demonstrated that facial appearance
could be highly indicative of depressive disorder. While previous methods based on the …

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 …

With the rapid advances in digital imaging and communication technologies, recently image
set classification has attracted significant attention and has been widely used in many real …

Collaborative representation methods, such as sparse subspace clustering (SSC) and
sparse representation-based classification (SRC), have achieved great success in face …

During the past several years, as one of the most successful applications of sparse coding
and dictionary learning, dictionary-based face recognition has received significant attention …

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

Many classification approaches first represent a test sample using the training samples of all
the classes. This collaborative representation is then used to label the test sample. It is a …