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 …

In video surveillance, face recognition (FR) systems are employed to detect individuals of
interest appearing over a distributed network of cameras. The performance of still-to-video …

Single sample per person (SSPP) face recognition with a contaminated biometric enrolment
database (SSPP-ce FR) is an emerging practical FR problem, where the SSPP in the …

Sparse representation-based classification (SRC) has been shown to achieve a high level of
accuracy in face recognition (FR). However, matching faces captured in unconstrained video …

This work investigates the problem of undersampled face recognition (ie, insufficient training
data) encountered in practical Internet-of-Things (IoT) applications. Insufficient and uncertain …

Single sample per person face recognition (SSPP FR) is one of the most challenging
problems in FR due to the extreme lack of enrolment data. To date, the most popular SSPP …

The purpose of this paper is to solve the problem of robust face recognition U+ 0028 FR U+
0029 with single sample per person U+ 0028 SSPP U+ 0029. In the scenario of FR with …

Collecting samples is one of the main difficulties for face recognition, for example, in most of
the real-world applications such as law enhancement, e-passport, and ID card identification …

Face Recognition (FR) problem is one of the significant fields in computer vision. FR is used
to identify the faces that appear over distributed cameras over the network. The problem of …

Deep learning models for still-to-video FR typically provide a low level of accuracy because
faces captured in unconstrained videos are matched against a reference gallery comprised …