Sparse representation has shown an attractive performance in a number of applications.
However, the available sparse representation methods still suffer from some problems, and …

Image classification is a fundamental component in modern computer vision systems, where
sparse representation-based classification has drawn a lot of attention due to its robustness …

The face almost always has an axis-symmetrical structure. However, as the face usually
does not have an absolutely frontal pose when it is imaged, the majority of face images are …

Sparse representation methods based on l 1 and/or l 2 regularization have shown promising
performance in different applications. Previous studies show that the l 1 regularization based …

Re-engineering (RE) of existing educational institutions (EI) with adoption of latest
technology trends (LTT) in form of artificial intelligence (AI) can be great effective in term of …

Abstract Description and classification of face images is a significant task of computer vision,
machine learning and pattern recognition communities. In the past, researchers have made …

Linear regression classification (LRC) has attracted a great amount of attention owning to its
promising performance in face recognition. However, its performance will dramatically …

Among all image representation and classification methods, sparse representation has
proven to be an extremely powerful tool. However, a limited number of training samples are …

Sparse representation, which uses a test sample to represent a linear combination of an
entire set of training samples, has achieved great success in face recognition, and it results …

Sparse representation based classification method has led to a wide variety of extensions of
representation based methods for face recognition. All of these methods partially reveal that …