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| 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 |
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| 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 |
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| 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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