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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Journal Article
“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
Random Faces Guided Sparse Many-to-One Encoder for Pose-Invariant Face Recognition
TL;DR: This paper builds a single-hidden-layer neural network with sparse constraint, to extract pose-invariant feature for face recognition in a supervised fashion, and enhances the discriminative capability of the proposed feature by using multiple random faces as the target values for multiple encoders.
Book ChapterDOI
Privacy of Facial Soft Biometrics: Suppressing Gender But Retaining Identity
Asem Othman,Arun Ross +1 more
TL;DR: This work focuses on masking the gender information in a face image with respect to an automated gender estimation scheme, while retaining its ability to be used by a face matcher.
Proceedings ArticleDOI
Bi-Shifting Auto-Encoder for Unsupervised Domain Adaptation
TL;DR: The proposed Bi-shifting Auto-Encoder network (BAE) can shift samples between domains and thus effectively deal with the domain discrepancy, and the promising results demonstrate that the proposed BAE can shift sample between domains.
Journal ArticleDOI
Forensic Face Photo-Sketch Recognition Using a Deep Learning-Based Architecture
TL;DR: The proposed framework is shown to reduce the error rate by 80.7% for viewed sketches and lowers the mean retrieval rank by 32.5% for real-world forensic sketches.
Proceedings ArticleDOI
Deep convolutional dynamic texture learning with adaptive channel-discriminability for 3D mask face anti-spoofing
TL;DR: This paper proposes a novel method for 3D mask face anti-spoofing, namely deep convolutional dynamic texture learning, which learns robust dynamic texture information from fine-grained deep Convolutional features.