Paper Abstract and Keywords |
Presentation |
2020-07-20 10:50
Development of Feature Extractor for Visible Light Iris Recognition Using Deep Learning Tetsuya Honda, Hironobu Takano (Toyama Pref. Univ.) ISEC2020-16 SITE2020-13 BioX2020-19 HWS2020-9 ICSS2020-3 EMM2020-13 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
Biometric authentication is superior to other personal authentication methods in terms of security
against theft and counterfeiting. Among biometric authentications, iris authentication can perform contactless authentication and has a higher authentication accuracy than other biometric modalities. When an iris image captured by a visible camera is used for authentication, the authentication accuracy may be decreased compared to that of a near-infrared iris image. Thus, it is required that the performance of visible iris authentication method is the same as those of personal authentication methods using the near-infrared iris image. We proposed the feature extractor using the multi-task CNN (Convolutional Neural Network) and compared with the authentication accuracy of the single-task CNN. From the experimental results, the EER (Equal Error Rate) of multi-task CNN was 0.9% lower than that of single-task CNN. The authentication performance was slightly improved by using the feature extractor of multi-task CNN. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
visible right iris authentication / CNN / multi-task / / / / / |
Reference Info. |
IEICE Tech. Rep., vol. 120, no. 114, BioX2020-19, pp. 15-19, July 2020. |
Paper # |
BioX2020-19 |
Date of Issue |
2020-07-13 (ISEC, SITE, BioX, HWS, ICSS, EMM) |
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 |
ISEC2020-16 SITE2020-13 BioX2020-19 HWS2020-9 ICSS2020-3 EMM2020-13 |
Conference Information |
Committee |
SITE ISEC HWS EMM BioX IPSJ-CSEC IPSJ-SPT ICSS |
Conference Date |
2020-07-20 - 2020-07-21 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
Online |
Topics (in Japanese) |
(See Japanese page) |
Topics (in English) |
Security, etc. |
Paper Information |
Registration To |
BioX |
Conference Code |
2020-07-SITE-ISEC-HWS-EMM-BioX-CSEC-SPT-ICSS |
Language |
Japanese |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
Development of Feature Extractor for Visible Light Iris Recognition Using Deep Learning |
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visible right iris authentication |
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CNN |
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multi-task |
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1st Author's Name |
Tetsuya Honda |
1st Author's Affiliation |
Toyama Prefectural University (Toyama Pref. Univ.) |
2nd Author's Name |
Hironobu Takano |
2nd Author's Affiliation |
Toyama Prefectural University (Toyama Pref. Univ.) |
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Speaker |
Author-1 |
Date Time |
2020-07-20 10:50:00 |
Presentation Time |
25 minutes |
Registration for |
BioX |
Paper # |
ISEC2020-16, SITE2020-13, BioX2020-19, HWS2020-9, ICSS2020-3, EMM2020-13 |
Volume (vol) |
vol.120 |
Number (no) |
no.112(ISEC), no.113(SITE), no.114(BioX), no.115(HWS), no.116(ICSS), no.117(EMM) |
Page |
pp.15-19 |
#Pages |
5 |
Date of Issue |
2020-07-13 (ISEC, SITE, BioX, HWS, ICSS, EMM) |
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