Deep face recognition: A survey
Deep learning applies multiple processing layers to learn representations of data with
multiple levels of feature extraction. This emerging technique has reshaped the research …
multiple levels of feature extraction. This emerging technique has reshaped the research …
Synthetic data for face recognition: Current state and future prospects
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 …
datasets advanced rapidly, leading to breakthroughs in face recognition accuracy. However …
Poisoning web-scale training datasets is practical
Deep learning models are often trained on distributed, webscale datasets crawled from the
internet. In this paper, we introduce two new dataset poisoning attacks that intentionally …
internet. In this paper, we introduce two new dataset poisoning attacks that intentionally …
Webface260m: A benchmark unveiling the power of million-scale deep face recognition
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) …
identities/260M faces (WebFace260M) and cleaned 2M identities/42M faces (WebFace42M) …
A survey on deep learning based face recognition
Deep learning, in particular the deep convolutional neural networks, has received
increasing interests in face recognition recently, and a number of deep learning methods …
increasing interests in face recognition recently, and a number of deep learning methods …
Arcface: Additive angular margin loss for deep face recognition
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 …
(DCNNs) for large-scale face recognition is the design of appropriate loss functions that can …
Vggface2: A dataset for recognising faces across pose and age
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 …
contains 3.31 million images of 9131 subjects, with an average of 362.6 images for each …
Iarpa janus benchmark-c: Face dataset and protocol
B Maze, J Adams, JA Duncan, N Kalka… - … on biometrics (ICB), 2018 - ieeexplore.ieee.org
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 …
facial recognition towards operation on fully unconstrained imagery, research has always …
Learning spatial attention for face super-resolution
General image super-resolution techniques have difficulties in recovering detailed face
structures when applying to low resolution face images. Recent deep learning based …
structures when applying to low resolution face images. Recent deep learning based …
How computers see gender: An evaluation of gender classification in commercial facial analysis services
MK Scheuerman, JM Paul, JR Brubaker - Proceedings of the ACM on …, 2019 - dl.acm.org
Investigations of facial analysis (FA) technologies-such as facial detection and facial
recognition-have been central to discussions about Artificial Intelligence's (AI) impact on …
recognition-have been central to discussions about Artificial Intelligence's (AI) impact on …