Kotaro, Mukai Graduate School of Sustainability Science Tottori University
Nakanishi, Isao Faculty of Engineering, Tottori University Researchers DB KAKEN
Biometric authentication
Fractal dimension
Calculation amount
The aim of this study is to authenticate individuals using an electroencephalogram (EEG) evoked by a stimulus. EEGs are highly confidential and enable continuous authentication during the use of or access to the given information or service. However, perceivable stimulation distracts the users from the activity they are carrying out while using the service. Therefore, ultrasound stimuli were chosen for EEG evocation. In our previous study, an Equal Error Rate (EER) of 0 % was achieved; however, there were some features which had not been evaluated. In this paper, we introduce a new type of feature, namely fractal dimension, as a nonlinear feature, and evaluate its verification performance on its own and in combination with other conventional features. As a result, an EER of 0 % was achieved when using five features and 14 electrodes, which accounted for 70 support vector machine (SVM) models. However, the construction of the 70 SVM models required extensive calculations. Thus, we reduced the number of SVM models to 24 while maintaining an EER = 0 %.
Content Type
Conference Paper
Journal Title
Proceedings of the 2020 IEEE Region 10 Conference (TENCON2020)
Current Journal Title
Proceedings of the 2020 IEEE Region 10 Conference (TENCON2020)
Published Date
Text Version
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K. Mukai, I Nakanishi. Introduction of Fractal Dimension Feature and Reduction of Calculation Amount in Person Authentication Using Evoked EEG by Ultrasound. Proceedings of the 2020 IEEE Region 10 Conference (TENCON2020). 2020
Faculty of Engineering/Graduate School of Engineering