Deep learning applies multiple processing layers to learn representations of data with
multiple levels of feature extraction. This emerging technique has reshaped the research …

Biometric systems have the goal of measuring and analyzing the unique physical or
behavioral characteristics of an individual. The main feature of biometric systems is the use …

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

Recent face recognition experiments on a major benchmark LFW show stunning
performance--a number of algorithms achieve near to perfect score, surpassing human …

Human faces in surveillance videos often suffer from severe image blur, dramatic pose
variations, and occlusion. In this paper, we propose a comprehensive framework based on …

Rapid progress in unconstrained face recognition has resulted in a saturation in recognition
accuracy for current benchmark datasets. While important for early progress, a chief …

Abstract Symmetric Positive Definite (SPD) matrix learning methods have become popular in
many image and video processing tasks, thanks to their ability to learn appropriate statistical …

The performance of face analysis and recognition systems depends on the quality of the
acquired face data, which is influenced by numerous factors. Automatically assessing the …

Face recognition has achieved great success in the last five years due to the development of
deep learning methods. However, deep convolutional neural networks (DCNNs) have been …

Binary feature descriptors such as local binary patterns (LBP) and its variations have been
widely used in many face recognition systems due to their excellent robustness and strong …