In recent years, researchers have proposed many deep learning (DL) methods for various
tasks, and particularly face recognition (FR) made an enormous leap using these …

Artificial intelligence (AI) is currently being utilized in a wide range of sophisticated
applications, but the outcomes of many AI models are challenging to comprehend and trust …

Deep neural networks (DNNs) are an indispensable machine learning tool despite the
difficulty of diagnosing what aspects of a model's input drive its decisions. In countless real …

Deep learning, in particular the deep convolutional neural networks, has received
increasing interests in face recognition recently, and a number of deep learning methods …

This paper targets on the problem of set to set recognition, which learns the metric between
two image sets. Images in each set belong to the same identity. Since images in a set can be …

The objective of this paper is to learn a compact representation of image sets for template-
based face recognition. We make the following contributions: first, we propose a network …

Face image quality can be defined as a measure of the utility of a face image to automatic
face recognition. In this paper, we propose (and compare) two methods for learning face …

Abstract Convolutional Neural Networks have provided state-of-the-art results in several
computer vision problems. However, due to a large number of parameters in CNNs, they …

A large scale study of the accuracy and efficiency of face detection algorithms on
unconstrained face imagery is presented. Nine different face detection algorithms are …

The growing use of control access systems based on face recognition shed light over the
need for even more accurate systems to detect face spoofing attacks. In this paper, an …