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
Deep Unsupervised Domain Adaptation for Face Recognition
TL;DR: Unsupervised transfer learning methods are adopted to alleviate the discrepancy between source and target face database and ensure the generalization ability of the model and utilize the massive amount of labeled facial images of source database to training the deep neural network at the same time.
Proceedings ArticleDOI
How to Train Triplet Networks with 100K Identities
Chong Wang,Xipeng Lan,Xue Zhang +2 more
TL;DR: Zhang et al. as mentioned in this paper proposed training triplet networks with subspace learning, which splits the space of all identities into subspaces consisting of only similar identities, and combined with the batch hard negative mining (OHNM) to find effective hard triplets.
Proceedings ArticleDOI
Makeup-robust face verification
TL;DR: This paper proposes a novel approach for makeup-robust face verification, by measuring correlations between face images in a meta subspace using canonical correlation analysis (CCA) and discriminative learning with the support vector machine (SVM) classifier.
Posted Content
Deeply Coupled Auto-encoder Networks for Cross-view Classification.
TL;DR: A simple but effective coupled neural network, called Deeply Coupled Autoencoder Networks (DCAN), which seeks to build two deep neural networks, coupled with each other in every corresponding layers, to preserve the local consistency and enhance its discriminative capability.
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WebCaricature: a benchmark for caricature face recognition
TL;DR: In this paper, a new caricature dataset is built, with the objective to facilitate research in caricature recognition, and a framework for caricature face recognition is presented to make a thorough analysis of the challenges of caricature recognition.