Face recognition using total loss function on face database with ID photos
With the development of deep neural networks, researchers have developed lots of algorithms related to face and achieved comparable results to human-level performance on several databases. However, few feature extraction models work well in the real world when the subject which is to be recognized h...
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sg-ntu-dr.10356-1513222021-06-22T04:35:51Z Face recognition using total loss function on face database with ID photos Cui, Dongshun Zhang, Guanghao Hu, Kai Han, Wei Huang, Guang-Bin Interdisciplinary Graduate School (IGS) School of Electrical and Electronic Engineering Energy Research Institute @ NTU (ERI@N) Engineering::Electrical and electronic engineering Face Recognition Face Recognition Benchmark With the development of deep neural networks, researchers have developed lots of algorithms related to face and achieved comparable results to human-level performance on several databases. However, few feature extraction models work well in the real world when the subject which is to be recognized has limited samples, for example, only one ID photo can be obtained before the face recognition task. To our best knowledge, there is no face database which contains ID photos and pictures from the real world for a subject simultaneously. To fill this gap, we collected 100 celebrities’ ID photos and their about 1000 stills or life pictures and formed a face database called FDID. Besides, we proposed a novel face recognition algorithm and evaluated it with this new database on the real-life videos. 2021-06-22T04:35:50Z 2021-06-22T04:35:50Z 2019 Journal Article Cui, D., Zhang, G., Hu, K., Han, W. & Huang, G. (2019). Face recognition using total loss function on face database with ID photos. Optics and Laser Technology, 110, 227-233. https://dx.doi.org/10.1016/j.optlastec.2017.10.016 0030-3992 0000-0002-9703-0120 https://hdl.handle.net/10356/151322 10.1016/j.optlastec.2017.10.016 2-s2.0-85032988939 110 227 233 en Optics and Laser Technology © 2017 Elsevier Ltd. All rights reserved. |
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Engineering::Electrical and electronic engineering Face Recognition Face Recognition Benchmark Cui, Dongshun Zhang, Guanghao Hu, Kai Han, Wei Huang, Guang-Bin Face recognition using total loss function on face database with ID photos |
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With the development of deep neural networks, researchers have developed lots of algorithms related to face and achieved comparable results to human-level performance on several databases. However, few feature extraction models work well in the real world when the subject which is to be recognized has limited samples, for example, only one ID photo can be obtained before the face recognition task. To our best knowledge, there is no face database which contains ID photos and pictures from the real world for a subject simultaneously. To fill this gap, we collected 100 celebrities’ ID photos and their about 1000 stills or life pictures and formed a face database called FDID. Besides, we proposed a novel face recognition algorithm and evaluated it with this new database on the real-life videos. |
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Interdisciplinary Graduate School (IGS) |
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Interdisciplinary Graduate School (IGS) Cui, Dongshun Zhang, Guanghao Hu, Kai Han, Wei Huang, Guang-Bin |
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Article |
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Cui, Dongshun Zhang, Guanghao Hu, Kai Han, Wei Huang, Guang-Bin |
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Cui, Dongshun |
title |
Face recognition using total loss function on face database with ID photos |
title_short |
Face recognition using total loss function on face database with ID photos |
title_full |
Face recognition using total loss function on face database with ID photos |
title_fullStr |
Face recognition using total loss function on face database with ID photos |
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Face recognition using total loss function on face database with ID photos |
title_sort |
face recognition using total loss function on face database with id photos |
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2021 |
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https://hdl.handle.net/10356/151322 |
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1703971245644578816 |