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Paper Abstract and Keywords
Presentation 2025-06-05 10:50
Investigation of A Personal Authentication Method Using Insole-Type Gait Sensors Based on Deep Learning
Etsushi Kumamoto, Kyosuke Fukamachi, Susumu Sato, Takashi Kawanami (KIT)
Abstract (in Japanese) (See Japanese page) 
(in English) Gait is a subconscious and difficult-to-forge behavior, making it suitable for biometric authentication. However, many existing studies rely on data collected over short periods, and the robustness of authentication models against temporal gait variation remains an issue. Focusing on an insole-type gait sensor developed by NEC that allows for long-term data collection and use, this study proposes a personal authentication method based on deep learning and conducts a comparative evaluation using short-term and long-term gait data. The model achieved a high accuracy with an equal error rate (EER) of 0.71% when trained and evaluated using only short-term data. Furthermore, by incorporating long-term gait data into the training set, the EER decreased to 0.28%.
Keyword (in Japanese) (See Japanese page) 
(in English) Gait / Biometric Authentication / Continuous Authentication / Long-term Data / / / /  
Reference Info. ITE Tech. Rep.
Paper #  
Date of Issue  
ISSN Online edition: ISSN 2424-1970
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Conference Information
Committee IST ME IEICE-IE IEICE-BioX IEICE-SIP IEICE-MI  
Conference Date 2025-06-05 - 2025-06-06 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To IEICE-BioX 
Conference Code 2025-06-IST-ME-IE-BioX-SIP-MI 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Investigation of A Personal Authentication Method Using Insole-Type Gait Sensors Based on Deep Learning 
Sub Title (in English)
Keyword(1) Gait  
Keyword(2) Biometric Authentication  
Keyword(3) Continuous Authentication  
Keyword(4) Long-term Data  
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1st Author's Name Etsushi Kumamoto  
1st Author's Affiliation Kanazawa Institute of Technology (KIT)
2nd Author's Name Kyosuke Fukamachi  
2nd Author's Affiliation Kanazawa Institute of Technology (KIT)
3rd Author's Name Susumu Sato  
3rd Author's Affiliation Kanazawa Institute of Technology (KIT)
4th Author's Name Takashi Kawanami  
4th Author's Affiliation Kanazawa Institute of Technology (KIT)
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Speaker Author-1 
Date Time 2025-06-05 10:50:00 
Presentation Time 25 minutes 
Registration for IEICE-BioX 
Paper #  
Volume (vol) vol.49 
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