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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References
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Proceedings ArticleDOI
SSPP-DAN: Deep domain adaptation network for face recognition with single sample per person
TL;DR: In the proposed approach, domain adaptation, feature extraction, and classification are performed jointly using a deep architecture with domain-adversarial training, but the SSPP characteristic of one training sample per class is insufficient to train the deep architecture.
Posted Content
Learning a Metric Embedding for Face Recognition using the Multibatch Method
TL;DR: In this article, the multibatch method was proposed for similarity learning and achieved state-of-the-art performance on the LFW benchmark using only 12 hours on a single Titan X GPU.
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Deep Heterogeneous Feature Fusion for Template-Based Face Recognition
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Proceedings ArticleDOI
Doppelganger Mining for Face Representation Learning
TL;DR: It is shown that Doppelganger mining, being inserted in the face representation learning process with joint prototype-based and exemplar-based supervision, significantly improves the discriminative power of learned face representations.
Proceedings ArticleDOI
Gender Privacy: An Ensemble of Semi Adversarial Networks for Confounding Arbitrary Gender Classifiers
TL;DR: In this article, an ensemble SAN model that generates a diverse set of perturbed outputs for a given input face image is designed to ensure that at least one of the perturbed output faces will confound an arbitrary, previously unseen gender classifier.