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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Journal ArticleDOI
Dynamic Feature Matching for Partial Face Recognition
TL;DR: This paper proposes a novel partial face recognition approach, called dynamic feature matching (DFM), which combines fully convolutional networks and sparse representation classification (SRC) to addresspartial face recognition problem regardless of various face sizes.
Proceedings ArticleDOI
Recursive Spatial Transformer (ReST) for Alignment-Free Face Recognition
TL;DR: The proposed Recursive Spatial Transformer (ReST) module into CNN is introduced, allowing face alignment to be jointly learned with face recognition in an end-to-end fashion, and is end- to-end learnable and adaptive to input, making it an effective alignment-free face recognition solution.
Proceedings ArticleDOI
Learning Discriminative Aggregation Network for Video-Based Face Recognition
TL;DR: Experimental results show that the DAN method for video face recognition can generate discriminative images from video clips and improve the overall recognition performance in both the speed and accuracy on three widely used datasets.
Posted Content
Triplet Similarity Embedding for Face Verification
TL;DR: Experiments show that the proposed algorithm outperforms state of the art methods in verification and identification metrics, while requiring less training time, on the recently released IJB-A dataset.
Journal ArticleDOI
Fine-grained face verification
TL;DR: A Deep Convolutional Maxout Network (DCMN) is developed which aim to tolerate the multi-modal intra-personal variations and distinguish fine-grained localized inter-personal facial details and the experimental results suggest that the proposed DCMN method significantly outperforms current techniques such as Deepface, DeepID2, and VGG-Face.