Deep face recognition: A survey
Mei Wang,Weihong Deng +1 more
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
Citations
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Journal ArticleDOI
FaceHack: Attacking Facial Recognition Systems Using Malicious Facial Characteristics
TL;DR: This work proposes the use of facial characteristics as triggers to backdoored facial recognition systems and substantiates that their triggers are undetectable by thoroughly testing them on state-of-the-art defense and detection mechanisms.
Posted Content
Two-Level Attention-based Fusion Learning for RGB-D Face Recognition
TL;DR: Comparative evaluations demonstrate that the proposed method outperforms other state-of-the-art approaches, including both traditional and deep neural network-based methods, on the challenging CurtinFaces and IIIT-D RGB-D benchmark databases, achieving classification accuracies over 98.2% and 99.3% respectively.
Journal ArticleDOI
A New Facial Authentication Pitfall and Remedy in Web Services
Dalton Cole,Sara Newman,Dan Lin +2 more
TL;DR: A new data poisoning attack that does not require to have any knowledge of the server-side and just needs a handful of malicious photo injections to enable an attacker to easily impersonate the victim in the existing facial authentication systems is demonstrated and a novel defensive approach called DEFEAT that leverages deep learning techniques to automatically detect such attacks is proposed.
Journal ArticleDOI
3D Face Recognition Based on an Attention Mechanism and Sparse Loss Function
Hongyan Zou,Xinyan Sun +1 more
TL;DR: Wang et al. as discussed by the authors presented a fast face recognition algorithm combining 3D point cloud face data with deep learning, focusing on key part of face for recognition with an attention mechanism, and reducing the coding space by the sparse loss function.
Journal ArticleDOI
IdentityDP: Differential private identification protection for face images
TL;DR: Li et al. as mentioned in this paper proposed IdentityDP, a face anonymization framework that combines a data-driven deep neural network with a differential privacy (DP) mechanism, which can effectively obfuscate the identityrelated information of faces, preserve significant visual similarity, and generate high-quality images that can be used for identity-agnostic computer vision tasks, such as detection, tracking, etc.
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.
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ImageNet Classification with Deep Convolutional Neural Networks
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Proceedings Article
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Karen Simonyan,Andrew Zisserman +1 more
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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
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