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| Presentation | 2021-12-16 11:00 Low-Resolution Iris Recognition with Image Super-Resolution for arbitrary magnification Tsubasa Bora (UEC), Takahiro Toizumi, Yuho Shoji, Yuka Ogino, Masato Tsukada (NEC), Masatsugu Ichino (UEC)  PRMU2021-26 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | A low-resolution iris image reduces iris recognition accuracy. Some conventional researches tackle low-resolution iris recognition using image super-resolution techniques. However, general image super-resolution methods drop personal identity information, and these regard super-resolution of different scales as independent tasks. In this paper, we propose low-resolution iris recognition based on super-resolution of arbitrary scale factors keeping a recognition accuracy. Our method utilizes a probability distribution to control a scale selection during training to suppress differences in recognition performance from different scale super-resolution. We show that our proposed method keeps the recognition accuracy by lower resolution than the conventional methods. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Biometrics / Iris recognition / Deep learning / Image super-resolution / CNN / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 121, no. 304, PRMU2021-26, pp. 13-18, Dec. 2021. | |
| Paper # | PRMU2021-26 | |
| Date of Issue | 2021-12-09 (PRMU) | |
| 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 | PRMU2021-26 |
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| Conference Information | |
| Committee | PRMU |
| Conference Date | 2021-12-16 - 2021-12-17 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Online |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Paper Information | |
| Registration To | PRMU |
| Conference Code | 2021-12-PRMU |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Low-Resolution Iris Recognition with Image Super-Resolution for arbitrary magnification |
| Sub Title (in English) | |
| Keyword(1) | Biometrics |
| Keyword(2) | Iris recognition |
| Keyword(3) | Deep learning |
| Keyword(4) | Image super-resolution |
| Keyword(5) | CNN |
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| 1st Author's Name | Tsubasa Bora |
| 1st Author's Affiliation | The University of Electro-Communications (UEC) |
| 2nd Author's Name | Takahiro Toizumi |
| 2nd Author's Affiliation | NEC Corporation (NEC) |
| 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 | Masato Tsukada |
| 5th Author's Affiliation | NEC Corporation (NEC) |
| 6th Author's Name | Masatsugu Ichino |
| 6th Author's Affiliation | The University of Electro-Communications (UEC) |
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| Speaker | Author-1 |
| Date Time | 2021-12-16 11:00:00 |
| Presentation Time | 15 minutes |
| Registration for | PRMU |
| Paper # | PRMU2021-26 |
| Volume (vol) | vol.121 |
| Number (no) | no.304 |
| Page | pp.13-18 |
| #Pages | 6 |
| Date of Issue | 2021-12-09 (PRMU) |
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