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

In the recent past, deep learning methods have demonstrated remarkable success for
supervised learning tasks in multiple domains including computer vision, natural language …

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

Racial bias is an important issue in biometric, but has not been thoroughly studied in deep
face recognition. In this paper, we first contribute a dedicated dataset called Racial Faces in …

In recent years, the performance of face verification systems has significantly improved using
deep convolutional neural networks (DCNNs). A typical pipeline for face verification includes …

This paper presents a Neural Aggregation Network (NAN) for video face recognition. The
network takes a face video or face image set of a person with a variable number of face …

Face recognition (FR) is one of the most popular and long-standing topics in computer
vision. With the recent development of deep learning techniques and large-scale datasets …

The unprecedented success of deep learning is largely dependent on the availability of
massive amount of training data. In many cases, these data are crowd-sourced and may …

Face recognition capabilities have recently made extraordinary leaps. Though this progress
is at least partially due to ballooning training set sizes–huge numbers of face images …

The availability of large annotated datasets and affordable computation power have led to
impressive improvements in the performance of convolutional neural networks (CNNs) on …