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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“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告
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A Survey of Deep Learning-Based Object Detection
TL;DR: This survey provides a comprehensive overview of a variety of object detection methods in a systematic manner, covering the one-stage and two-stage detectors, and lists the traditional and new applications.
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Deep neural network concepts for background subtraction:A systematic review and comparative evaluation
TL;DR: In this article, the authors provide a review of deep neural network concepts in background subtraction for novices and experts in order to analyze this success and to provide further directions.
MORPH: A Longitudinal Image Database of Normal Adult Age-Progression.
Karl Ricanek,Tamirat Tesafaye +1 more
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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Proceedings ArticleDOI
One-Shot Face Recognition via Generative Learning
TL;DR: A novel generative model attempting to synthesize meaningful data for one-shot classes by adapting the data variances from other normal classes is developed, which significantly improves the recognition coverage rate and keeps an overall Top-1 accuracy at 99:80% for the normal classes.
Proceedings ArticleDOI
Hard Example Mining with Auxiliary Embeddings
Evgeny Smirnov,Elizaveta Ivanova,Aleksandr Melnikov,Ilya Kalinovskiy,Andrei Oleinik,Eugene Luckyanets +5 more
TL;DR: The experiments on the challenging Disguised Faces in the Wild dataset show that hard example mining with auxiliary embeddings improves the discriminative power of learned representations.
Posted Content
Feature Transfer Learning for Deep Face Recognition with Long-Tail Data.
TL;DR: This paper proposes to handle long-tail classes in the training of a face recognition engine by augmenting their feature space under a center-based feature transfer framework, which allows smooth visual interpolation, which demonstrates disentanglement to preserve identity of a class while augmenting its feature space with non-identity variations.
Journal ArticleDOI
Unconstrained Face Recognition Using a Set-to-Set Distance Measure on Deep Learned Features
TL;DR: This paper proposes a novel set-to-set (S2S) distance measure to calculate the similarity between two sets with the aim to improve the recognition accuracy for faces with real-world challenges, such as extreme poses or severe illumination conditions.
Journal ArticleDOI
Compressive Binary Patterns: Designing a Robust Binary Face Descriptor with Random-Field Eigenfilters
Weihong Deng,Jiani Hu,Jun Guo +2 more
TL;DR: Surprisingly, the results obtained from experiments show that the scattering CBP (SCBP) descriptor, which is handcrafted by only 6 optimal eigenfilters under restrictive assumptions, outperforms the state-of-the-art learning-based face descriptors in terms of both matching accuracy and robustness.