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

Over the past years, deep learning capabilities and the availability of large-scale training
datasets advanced rapidly, leading to breakthroughs in face recognition accuracy. However …

Deep learning models are often trained on distributed, web-scale datasets crawled from the
internet. In this paper, we introduce two new dataset poisoning attacks that intentionally …

In this paper, we contribute a new million-scale face benchmark containing noisy 4M
identities/260M faces (WebFace260M) and cleaned 2M identities/42M faces (WebFace42M) …

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

One of the main challenges in feature learning using Deep Convolutional Neural Networks
(DCNNs) for large-scale face recognition is the design of appropriate loss functions that can …

In this paper, we introduce a new large-scale face dataset named VGGFace2. The dataset
contains 3.31 million images of 9131 subjects, with an average of 362.6 images for each …

Although considerable work has been done in recent years to drive the state of the art in
facial recognition towards operation on fully unconstrained imagery, research has always …

General image super-resolution techniques have difficulties in recovering detailed face
structures when applying to low resolution face images. Recent deep learning based …

Photorealistic frontal view synthesis from a single face image has a wide range of
applications in the field of face recognition. Although data-driven deep learning methods …