The pose, illumination and facial expression discrepancies between two face images are the
key challenges in face recognition. The deep Convolutional Neural Networks (CNNs) and …

Grouped sparse representation classification methods (GSRCMs) have been attracted much
attention by scholars, especially in face recognition. However, pervious literatures of …

Sparse representation based face recognition is the most recent technique used, this
technique first codes a testing sample as a sparse linear combination of all the training …

Sparse representation based classification (SRC) has recently been proposed for robust
face recognition. To deal with occlusion, SRC introduces an identity matrix as an occlusion …

In this paper, we present a novel high-frequency facial feature and a high-frequency-based
sparse representation classification to tackle single sample face recognition (SSFR) under …

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 based Classification (SRC) has emerged as a new paradigm for
solving recognition problems. This paper presents a constraint sampling feature extraction …

Sparse representation is of great significance to the research of face recognition. Due to
factors such as illumination, angle, and facial features, different face images of the same …

Sparse representation receives a lot of attention as a representation method of signals in
recent years. The sparse representation-based classification (SRC) applies sparse …

Abstract Face recognition with Kernel Sparse Representation based Classification (KSRC)
has shown its great classification performance, and as an extension of Sparse …