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Paper Abstract and Keywords
Presentation 2023-03-02 11:00
A Study on Training Methods for Iris Recognition that Can Control Balance of Learning between Network and Loss Function
Rikuto Otsuka (UEC), Yuho Shoji, Yuka Ogino, Takahiro Toizumi (NEC), Masatsugu Ichino (UEC) BioX2022-72 CNR2022-38
Abstract (in Japanese) (See Japanese page) 
(in English) In this paper, we propose a training method for iris recognition by deep learning that focuses on training weight parameters of a network such as kernels of convolutional layers rather than weight parameters of a loss function such as ArcFace. In iris recognition by deep learning, a network is trained with a loss function to extract feature vectors from iris images, and the trained network is used for the recognition phase. Many previous studies on iris recognition do not distinguish between the weight parameters of the network and the weight parameters of the loss function, and use the same amount of updates for training, despite the roles and the number of parameters being generally different. When training with the same updates, the loss function converges faster than the network, and the network will be under learning since the loss function has fewer weight parameters than the network has. As a result, it may cause a degradation of the performance of iris recognition. Therefore, we train the network more strongly than the loss function to improve the recognition performance of iris recognition.
Keyword (in Japanese) (See Japanese page) 
(in English) Iris Recognition / Deep Learning / / / / / /  
Reference Info. IEICE Tech. Rep., vol. 122, no. 394, BioX2022-72, pp. 59-64, March 2023.
Paper # BioX2022-72 
Date of Issue 2023-02-22 (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 BioX2022-72 CNR2022-38

Conference Information
Committee CNR BioX  
Conference Date 2023-03-01 - 2023-03-02 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To BioX 
Conference Code 2023-03-CNR-BioX 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A Study on Training Methods for Iris Recognition that Can Control Balance of Learning between Network and Loss Function 
Sub Title (in English)  
Keyword(1) Iris Recognition  
Keyword(2) Deep Learning  
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1st Author's Name Rikuto Otsuka  
1st Author's Affiliation he University of Electro-Communications (UEC)
2nd Author's Name Yuho Shoji  
2nd Author's Affiliation NEC Corporation (NEC)
3rd Author's Name Yuka Ogino  
3rd Author's Affiliation NEC Corporation (NEC)
4th Author's Name Takahiro Toizumi  
4th Author's Affiliation NEC Corporation (NEC)
5th Author's Name Masatsugu Ichino  
5th Author's Affiliation he University of Electro-Communications (UEC)
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Speaker Author-1 
Date Time 2023-03-02 11:00:00 
Presentation Time 30 minutes 
Registration for BioX 
Paper # BioX2022-72, CNR2022-38 
Volume (vol) vol.122 
Number (no) no.394(BioX), no.395(CNR) 
Page pp.59-64 
#Pages 6 
Date of Issue 2023-02-22 (BioX, CNR) 

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