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Deep face recognition: A survey

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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.
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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.

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Citations
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Journal ArticleDOI

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.
Journal ArticleDOI

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.

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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Posted Content

Rethinking Feature Discrimination and Polymerization for Large-scale Recognition.

TL;DR: The congenerous cosine (COCO) algorithm is proposed to simultaneously optimize the cosine similarity among data and inherits the softmax property to make inter-class features discriminative as well as shares the idea of class centroid in metric learning.
Proceedings ArticleDOI

Mitigating Bias in Face Recognition Using Skewness-Aware Reinforcement Learning

TL;DR: Zhang et al. as mentioned in this paper proposed a reinforcement learning based race balance network (RL-RBN) to learn balanced performance for different races based on large margin losses and formulated the process of finding the optimal margins for non-Caucasians as a Markov decision process and employed deep Q-learning to learn policies for an agent to select appropriate margin by approximating the Q-value function.
Posted Content

Adversarial Generative Nets: Neural Network Attacks on State-of-the-Art Face Recognition.

TL;DR: This paper shows how to create eyeglasses that, when worn, can succeed in targeted or untargeted attacks while improving on previous work in one or more of three facets: inconspicuousness to onlooking observers, robustness of the attack against proposed defenses, and scalability in the sense of decoupling eyeglass creation from the subject who will wear them.
Proceedings Article

Unravelling Robustness of Deep Learning Based Face Recognition Against Adversarial Attacks

TL;DR: In this article, the authors investigated the impact of adversarial attacks on the robustness of DNN-based face recognition models and proposed several effective countermeasures to mitigate the impact.
Journal ArticleDOI

Face alignment in-the-wild: A Survey

TL;DR: This survey presents an up-to-date critical review of the existing literatures on face alignment, focusing on those methods addressing overall difficulties and challenges of this topic under uncontrolled conditions.
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