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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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Journal ArticleDOI

Globally Maximizing, Locally Minimizing: Unsupervised Discriminant Projection with Applications to Face and Palm Biometrics

TL;DR: In this paper, an unsupervised discriminant projection (UDP) technique for dimensionality reduction of high-dimensional data in small sample size cases is proposed, which can be seen as a linear approximation of a multimanifolds-based learning framework taking into account both the local and nonlocal quantities.
Proceedings ArticleDOI

Face recognition with learning-based descriptor

TL;DR: This work proposes a pose-adaptive matching method that uses pose-specific classifiers to deal with different pose combinations of the matching face pair, and finds that a simple normalization mechanism after PCA can further improve the discriminative ability of the descriptor.
Proceedings ArticleDOI

Regressing Robust and Discriminative 3D Morphable Models with a Very Deep Neural Network

TL;DR: This paper used a CNN to regress 3DMM shape and texture parameters directly from an input photo and achieved state-of-the-art results on the LFW, YTF and IJB-A benchmarks.
Patent

L2 constrained softmax loss for discriminative face verification

TL;DR: This paper adds an L2-constraint to the feature descriptors which restricts them to lie on a hypersphere of a fixed radius and shows that integrating this simple step in the training pipeline significantly boosts the performance of face verification.
Journal ArticleDOI

Face Recognition Performance: Role of Demographic Information

TL;DR: It is shown that an alternative to dynamic face matcher selection is to train face recognition algorithms on datasets that are evenly distributed across demographics, as this approach offers consistently high accuracy across all cohorts.
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