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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Journal ArticleDOI
Distance metric optimization driven convolutional neural network for age invariant face recognition
TL;DR: A novel distance metric optimization driven learning approach that integrates these traditional steps via a deep convolutional neural network, which learns feature representations and the decision function in an end-to-end way, and can be optimized simultaneously by backward propagation.
Proceedings ArticleDOI
UV-GAN: Adversarial Facial UV Map Completion for Pose-Invariant Face Recognition
TL;DR: In this paper, Li et al. proposed a framework for training deep convolutional neural network (DCNN) to complete the facial UV map extracted from in-the-wild images.
Book ChapterDOI
Cross-Database Face Antispoofing with Robust Feature Representation
TL;DR: A robust representation integrating deep texture features and face movement cue like eye-blink as countermeasures for presentation attacks like photos and replays for face spoof attacks is proposed.
Proceedings ArticleDOI
FaceID-GAN: Learning a Symmetry Three-Player GAN for Identity-Preserving Face Synthesis
TL;DR: FaceID-GAN is able to generate faces of arbitrary viewpoint while preserve identity, outperforming recent advanced approaches and substantially alleviating training difficulty of GAN.
Proceedings ArticleDOI
Latent Factor Guided Convolutional Neural Networks for Age-Invariant Face Recognition
Yandong Wen,Zhifeng Li,Yu Qiao +2 more
TL;DR: This work proposes a novel deep face recognition framework to learn the ageinvariant deep face features through a carefully designed CNN model, and is the first attempt to show the effectiveness of deep CNNs in advancing the state-of-the-art of AIFR.