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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TL;DR: This paper empirically verifies the superiority of the early softmax desaturation, and proposes Noisy Softmax to mitigating this early saturation issue by injecting annealed noise in softmax during each iteration and improves the generalization ability of CNN model by regularization.
Posted Content
Recover Canonical-View Faces in the Wild with Deep Neural Networks
TL;DR: This paper proposes a new deep learning framework that can recover the canonical view of face images, which dramatically reduces the intra-person variances, while maintaining the inter-person discriminativeness.
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One-shot Face Recognition by Promoting Underrepresented Classes
Yandong Guo,Lei Zhang +1 more
TL;DR: The problem of training large-scale face identification model with imbalanced training data is studied, and a novel supervision signal called underrepresented-classes promotion loss is proposed, which aligns the norms of the weight vectors of the one-shot classes to those of the normal classes.
Face Recognition Vendor Test (FRVT) Performance of Face Identification Algorithms NIST IR 8009
Patrick J. Grother,Mei L. Ngan +1 more
Proceedings ArticleDOI
An End-to-End System for Unconstrained Face Verification with Deep Convolutional Neural Networks
TL;DR: In this paper, an end-to-end system for unconstrained face verification based on deep convolutional neural networks (DCNNs) is presented, which consists of three modules for face detection, alignment and verification.