フルテキストファイル
著者
Miyake, Takahiro Graduate School of Sustainability Sciences Tottori University
Kinjo, Nozomu Graduate School of Sustainability Sciences Tottori University
Nakanishi, Isao Faculty of Engineering, Tottori University 研究者総覧 KAKEN
キーワード
Biometric authentication
Invisible stimulation Machine Learning
Electroencephalogram (EEG)
Wavelet Transform
抄録
In this study, we propose the authentication of individuals using electroencephalograms (EEGs) evoked by the application of invisible visual stimuli. In our previous study, we introduced a wavelet transform, which is a time-frequency analysis method, and applied it to extract features, including time information, to enable more accurate discrimination between individuals. An equal error rate (EER) of 9.4 % was achieved using Euclidean distance matching. In this paper, we introduce a machine learning-based approach in order to further improve the verification performance. An EER of 8.1 % is achieved by the proposed method after training the constituent neural networks using ensemble learning with 30 networks.
資料タイプ
会議資料
掲載誌名
Proceedings of the 2020 IEEE Region 10 Conference (TENCON2020)
最新掲載誌名
Proceedings of the 2020 IEEE Region 10 Conference (TENCON2020)
発行日
2020-11
著者版フラグ
著者版
著作権表記
© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
掲載情報
T. Miyake, N. Kinjo, I.Nakanishi. Wavelet Transform and Machine Learning-Based Biometric Authentication Using EEG Evoked by Invisible Visual Stimuli. Proceedings of the 2020 IEEE Region 10 Conference (TENCON2020). 2020.
部局名
工学部・工学研究科
言語
英語