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Paper Abstract and Keywords
Presentation 2017-10-12 14:35
Improvement of Accuracy by Machine Learning for Personal Authentication using High Frequency Intra-Body Propagation Characteristics
Shun Onoda, Takahiro Yoshida, Seiichiro Hangai (TUS) BioX2017-26
Abstract (in Japanese) (See Japanese page) 
(in English) As one of biometrics that can perform continuous personal authentication only by handling equipment, the personal authentication method using intra-body-frequency characteristics between two fingers have been researching in our laboratory. However, in our previous study, the authentication accuracy with verification method using the Manhattan distance for spectra of the intra-body-frequency characteristics (pass-through / reflection) measured by VNA was very low, e.g. the equal error rate (EER) of the verification using reflection characteristic S22 by the nine subjects was 25.1%. Therefore, in this study, we applied logistic regression, which is one of machine learning method, to the verification in order to improve the verification performance.
As a result, the 5.8% EER was archived by applying the logistic regression, that was 19.3 points improvement. It was found that the logistic regression was also effective in the verification using intra-body-propagation characteristics.
Keyword (in Japanese) (See Japanese page) 
(in English) Biometrics / intra-body-propagation characteristics / logistic regression / / / / /  
Reference Info. IEICE Tech. Rep., vol. 117, no. 236, BioX2017-26, pp. 7-10, Oct. 2017.
Paper # BioX2017-26 
Date of Issue 2017-10-05 (BioX) 
ISSN Print edition: ISSN 0913-5685    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)
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Conference Information
Committee BioX  
Conference Date 2017-10-12 - 2017-10-13 
Place (in Japanese) (See Japanese page) 
Place (in English) Nobumoto Ohama Memorial Hall 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Biometrics, etc. 
Paper Information
Registration To BioX 
Conference Code 2017-10-BioX 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Improvement of Accuracy by Machine Learning for Personal Authentication using High Frequency Intra-Body Propagation Characteristics 
Sub Title (in English)  
Keyword(1) Biometrics  
Keyword(2) intra-body-propagation characteristics  
Keyword(3) logistic regression  
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1st Author's Name Shun Onoda  
1st Author's Affiliation Tokyo University of Science (TUS)
2nd Author's Name Takahiro Yoshida  
2nd Author's Affiliation Tokyo University of Science (TUS)
3rd Author's Name Seiichiro Hangai  
3rd Author's Affiliation Tokyo University of Science (TUS)
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Speaker Author-1 
Date Time 2017-10-12 14:35:00 
Presentation Time 25 minutes 
Registration for BioX 
Paper # BioX2017-26 
Volume (vol) vol.117 
Number (no) no.236 
Page pp.7-10 
#Pages 4 
Date of Issue 2017-10-05 (BioX) 

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