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
Citations
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TL;DR: It is concluded that the problem of age-progression on face recognition (FR) is not unique to the algorithm used in this work, and the efficacy of this algorithm is evaluated against the variables of gender and racial origin.
References
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Presentation Attack Detection Methods for Face Recognition Systems: A Comprehensive Survey
TL;DR: This paper describes the various aspects of face presentation attacks, including different types of face artifacts, state-of-the-art PAD algorithms and an overview of the respective research labs working in this domain, vulnerability assessments and performance evaluation metrics, the outcomes of competitions, the availability of public databases for benchmarking new P AD algorithms in a reproducible manner, and a summary of the relevant international standardization in this field.
Proceedings ArticleDOI
Hybrid Deep Learning for Face Verification
Yi Sun,Xiaogang Wang,Xiaoou Tang +2 more
TL;DR: This work proposes a hybrid convolutional network-Restricted Boltzmann Machine model for face verification in wild conditions to directly learn relational visual features, which indicate identity similarities, from raw pixels of face pairs with a hybrid deep network.
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A Comprehensive Survey on Pose-Invariant Face Recognition
Changxing Ding,Dacheng Tao +1 more
TL;DR: The inherent difficulties in PIFR are discussed and a comprehensive review of established techniques are presented, that is, pose-robust feature extraction approaches, multiview subspace learning approaches, face synthesis approaches, and hybrid approaches.
Proceedings ArticleDOI
3D Face Reconstruction by Learning from Synthetic Data
TL;DR: In this article, a CNN-based approach is proposed for reconstructing a 3D face from a single image, which is based on a convolutional-neural-network (CNN) architecture.
Proceedings ArticleDOI
When Face Recognition Meets with Deep Learning: An Evaluation of Convolutional Neural Networks for Face Recognition
Guosheng Hu,Yongxin Yang,Dong Yi,Josef Kittler,William J. Christmas,Stan Z. Li,Timothy M. Hospedales +6 more
TL;DR: An extensive evaluation of CNN-based face recognition systems (CNN-FRS) and proposes three CNN architectures which are the first reported architectures trained using LFW data to make the work easily reproducible.