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Article

Efficiently Updating ECG-Based Biometric Authentication Based on Incremental Learning

1
Department of Computer Engineering, Kwangwoon University, Seoul 01897, Korea
2
School of Electrical Engineering, College of Creative Engineering, Kookmin University, Seoul 02707, Korea
3
AI Lab, LG Electronics, Seoul 06763, Korea
*
Authors to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editors: Paddy J. French and Marios Sophocleous
Sensors 2021, 21(5), 1568; https://doi.org/10.3390/s21051568
Received: 31 December 2020 / Revised: 5 February 2021 / Accepted: 12 February 2021 / Published: 24 February 2021
Recently, the interest in biometric authentication based on electrocardiograms (ECGs) has increased. Nevertheless, the ECG signal of a person may vary according to factors such as the emotional or physical state, thus hindering authentication. We propose an adaptive ECG-based authentication method that performs incremental learning to identify ECG signals from a subject under a variety of measurement conditions. An incremental support vector machine (SVM) is adopted for authentication implementing incremental learning. We collected ECG signals from 11 subjects during 10 min over six days and used the data from days 1 to 5 for incremental learning, and those from day 6 for testing. The authentication results show that the proposed system consistently reduces the false acceptance rate from 6.49% to 4.39% and increases the true acceptance rate from 61.32% to 87.61% per single ECG wave after incremental learning using data from the five days. In addition, the authentication results tested using data obtained a day after the latest training show the false acceptance rate being within reliable range (3.5–5.33%) and improvement of the true acceptance rate (70.05–87.61%) over five days. View Full-Text
Keywords: ECG; authentication; biometrics; incremental learning; SVM; incremental SVM ECG; authentication; biometrics; incremental learning; SVM; incremental SVM
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MDPI and ACS Style

Kim, J.; Yang, G.; Kim, J.; Lee, S.; Kim, K.K.; Park, C. Efficiently Updating ECG-Based Biometric Authentication Based on Incremental Learning. Sensors 2021, 21, 1568. https://doi.org/10.3390/s21051568

AMA Style

Kim J, Yang G, Kim J, Lee S, Kim KK, Park C. Efficiently Updating ECG-Based Biometric Authentication Based on Incremental Learning. Sensors. 2021; 21(5):1568. https://doi.org/10.3390/s21051568

Chicago/Turabian Style

Kim, Junmo, Geunbo Yang, Juhyeong Kim, Seungmin Lee, Ko K. Kim, and Cheolsoo Park. 2021. "Efficiently Updating ECG-Based Biometric Authentication Based on Incremental Learning" Sensors 21, no. 5: 1568. https://doi.org/10.3390/s21051568

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