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Paper Abstract and Keywords
Presentation 2021-03-02 14:50
A Face Identification System Based on Self-Supervised Learning Using Triplet-based Variational Autoencoder
Yuta Hagio, Yutaka Kaneko, Yuta Hoshi, Marina Kamimura, Yasuhiro Murasaki, Masao Yamatomo (NHK) BioX2020-46 CNR2020-19
Abstract (in Japanese) (See Japanese page) 
(in English) In this paper, we propose a face image identification system based on self-supervised learning for IoT devices and companion robots used in the home. Our system automatically constructs a triplet-based training dataset, which consists of two face images of the same person and one face image of a different person. In addition, a face image discriminator based on Triplet-based Variational Autoencoder is trained using the constructed dataset. Experimental results show that our system could automatically construct a training data, and about 98% of the constructed data was in the correct format, consisting of two face images of the same person and one face image of a different person. We also confirmed that our system could identify four face images with 94.2% accuracy and six face images with 95.2% accuracy, by training the constructed dataset.
Keyword (in Japanese) (See Japanese page) 
(in English) Face Identification / Self-Supervised Learning / Variational Autoencoder / Internet of Things / Companion Robots / / /  
Reference Info. IEICE Tech. Rep., vol. 120, no. 394, CNR2020-19, pp. 32-37, March 2021.
Paper # CNR2020-19 
Date of Issue 2021-02-23 (BioX, CNR) 
ISSN Online edition: ISSN 2432-6380
Copyright
and
reproduction		 All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF BioX2020-46 CNR2020-19

Conference Information
Committee BioX CNR  
Conference Date 2021-03-02 - 2021-03-02 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To CNR 
Conference Code 2021-03-BioX-CNR 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A Face Identification System Based on Self-Supervised Learning Using Triplet-based Variational Autoencoder 
Sub Title (in English)  
Keyword(1) Face Identification  
Keyword(2) Self-Supervised Learning  
Keyword(3) Variational Autoencoder  
Keyword(4) Internet of Things  
Keyword(5) Companion Robots  
Keyword(6)  
Keyword(7)  
Keyword(8)  
1st Author's Name Yuta Hagio  
1st Author's Affiliation Japan Broadcasting Corporation (NHK)
2nd Author's Name Yutaka Kaneko  
2nd Author's Affiliation Japan Broadcasting Corporation (NHK)
3rd Author's Name Yuta Hoshi  
3rd Author's Affiliation Japan Broadcasting Corporation (NHK)
4th Author's Name Marina Kamimura  
4th Author's Affiliation Japan Broadcasting Corporation (NHK)
5th Author's Name Yasuhiro Murasaki  
5th Author's Affiliation Japan Broadcasting Corporation (NHK)
6th Author's Name Masao Yamatomo  
6th Author's Affiliation Japan Broadcasting Corporation (NHK)
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Speaker Author-1 
Date Time 2021-03-02 14:50:00 
Presentation Time 20 minutes 
Registration for CNR 
Paper # BioX2020-46, CNR2020-19 
Volume (vol) vol.120 
Number (no) no.393(BioX), no.394(CNR) 
Page pp.32-37 
#Pages 6 
Date of Issue 2021-02-23 (BioX, CNR) 

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