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Paper Abstract and Keywords
Presentation 2022-09-14 10:15
Evaluation of Loss Functions for Low-Resolution Iris Recognition Using Deep Learning
Rikuto Otsuka, Tsubasa Bora (UEC), Yuho Shoji, Yuka Ogino, Takahiro Toizumi (NEC), Ichino Masatsugu (UEC) PRMU2022-11
Abstract (in Japanese) (See Japanese page) 
(in English) In this paper, we report the evaluation results of loss functions for low-resolution iris recognition using deep learning. In recent years, many loss functions for feature extractors using deep learning have been proposed, particularly on face recognition. On the other hand, unlike face recognition, research on iris recognition has a problem that loss functions have not been enough compared or investigated. Therefore, in this paper, we train feature extractors for iris recognition by using 12 types of loss functions proposed for face recognition and compare their performance. Furthermore, we search loss functions that are robust to low-resolution iris recognition by downsampling the resolution of the input images. The evaluation results show that CosFace is the best loss function on large datasets and Triplet loss is the best on small datasets for low-resolution iris recognition.
Keyword (in Japanese) (See Japanese page) 
(in English) Iris Recognition / Low-Resolution Iris Recognition / Loss Function / Deep Learning / / / /  
Reference Info. IEICE Tech. Rep., vol. 122, no. 181, PRMU2022-11, pp. 7-12, Sept. 2022.
Paper # PRMU2022-11 
Date of Issue 2022-09-07 (PRMU) 
ISSN Online edition: ISSN 2432-6380
Copyright
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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)
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Conference Information
Committee PRMU  
Conference Date 2022-09-14 - 2022-09-15 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English) Deep generative model 
Paper Information
Registration To PRMU 
Conference Code 2022-09-PRMU 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Evaluation of Loss Functions for Low-Resolution Iris Recognition Using Deep Learning 
Sub Title (in English)  
Keyword(1) Iris Recognition  
Keyword(2) Low-Resolution Iris Recognition  
Keyword(3) Loss Function  
Keyword(4) Deep Learning  
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1st Author's Name Rikuto Otsuka  
1st Author's Affiliation The University of Electro-Communications (UEC)
2nd Author's Name Tsubasa Bora  
2nd Author's Affiliation The University of Electro-Communications (UEC)
3rd Author's Name Yuho Shoji  
3rd Author's Affiliation NEC Corporation (NEC)
4th Author's Name Yuka Ogino  
4th Author's Affiliation NEC Corporation (NEC)
5th Author's Name Takahiro Toizumi  
5th Author's Affiliation NEC Corporation (NEC)
6th Author's Name Ichino Masatsugu  
6th Author's Affiliation The University of Electro-Communications (UEC)
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Speaker Author-1 
Date Time 2022-09-14 10:15:00 
Presentation Time 15 minutes 
Registration for PRMU 
Paper # PRMU2022-11 
Volume (vol) vol.122 
Number (no) no.181 
Page pp.7-12 
#Pages
Date of Issue 2022-09-07 (PRMU) 


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