Deep face recognition: A survey
Mei Wang,Weihong Deng +1 more
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TLDR
A comprehensive review of the recent developments on deep face recognition can be found in this paper, covering broad topics on algorithm designs, databases, protocols, and application scenes, as well as the technical challenges and several promising directions.About:
This article is published in Neurocomputing.The article was published on 2021-03-14 and is currently open access. It has received 353 citations till now. The article focuses on the topics: Deep learning & Feature extraction.read more
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Book ChapterDOI
Digital Face Manipulation in Biometric Systems
TL;DR: In this article , the impact of face image manipulation on face recognition technologies is discussed and the basic processes and key components of biometric systems are briefly introduced with particular emphasis on facial recognition, and face manipulation detection scenarios and concepts of how to integrate detection methods to face recognition systems are discussed.
Journal ArticleDOI
Metadata based need-to-know view in large-scale video surveillance systems
TL;DR: A privacy-aware and need-to-know access control framework built on fine-grained data properties, extracted from surveillance data, which must conform to the explicitly defined purpose of the observers is presented.
Journal ArticleDOI
The Study of Mathematical Models and Algorithms for Face Recognition in Images Using Python in Proctoring System
Ardak Nurpeisova,Anargul Shaushenova,Z. Mutalova,Zhandos Zulpykhar,M.B. Ongarbayeva,Shakizada Niyazbekova,Alexander Semenov,Leila A. Maisigova +7 more
TL;DR: The article analyzes the possibility and rationality of using proctoring technology in remote monitoring of the progress of university students as a tool for identifying a student and presents algorithms for solving computer vision problems.
Journal ArticleDOI
Towards Transferable Adversarial Attack against Deep Face Recognition
Yaoyao Zhong,Weihong Deng +1 more
TL;DR: DFANet as mentioned in this paper is a dropout-based method used in convolutional layers, which can increase the diversity of surrogate models and obtain ensemble-like effects to improve the transferability of feature-level adversarial examples.
Journal ArticleDOI
Evaluating Impact of Race in Facial Recognition across Machine Learning and Deep Learning Algorithms
James Coe,Mustafa Atay +1 more
TL;DR: In this article, the authors evaluate the impact of race in facial recognition across two types of algorithms, namely, machine learning and deep learning, and compare the results of two kinds of algorithms and compare their accuracy, metrics, miss rates, and performances to observe which algorithms mitigate racial bias the most.
References
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Proceedings ArticleDOI
Deep Residual Learning for Image Recognition
TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
Proceedings Article
ImageNet Classification with Deep Convolutional Neural Networks
TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
TL;DR: This work investigates the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting using an architecture with very small convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers.
Proceedings ArticleDOI
Going deeper with convolutions
Christian Szegedy,Wei Liu,Yangqing Jia,Pierre Sermanet,Scott Reed,Dragomir Anguelov,Dumitru Erhan,Vincent Vanhoucke,Andrew Rabinovich +8 more
TL;DR: Inception as mentioned in this paper is a deep convolutional neural network architecture that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14).
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Generative Adversarial Nets
Ian Goodfellow,Jean Pouget-Abadie,Mehdi Mirza,Bing Xu,David Warde-Farley,Sherjil Ozair,Aaron Courville,Yoshua Bengio +7 more
TL;DR: A new framework for estimating generative models via an adversarial process, in which two models are simultaneously train: a generative model G that captures the data distribution and a discriminative model D that estimates the probability that a sample came from the training data rather than G.