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arXiv 1907.00366cs.CRcs.LGeess.SPstat.ML

An Enhanced Electrocardiogram Biometric Authentication System Using Machine Learning

Khalifa University of Science and Technology(哈利法科技大学);Emirates ICT Innovation Centre (EBTIC)(阿联酋信息通信技术创新中心);Università degli Studi di Milano(米兰大学);Birkbeck College, University of London(伦敦大学伯克贝克学院);Defence Academy of the United Kingdom(英国国防学院)

Ebrahim Al Alkeem, Song-Kyoo Kim, Chan Yeob Yeun, M. Jamal Zemerly, Kin Poon, Paul D. Yoo

英文摘要:

Traditional authentication systems use alphanumeric or graphical passwords, or token-based techniques that require "something you know and something you have". The disadvantages of these systems include the risks of forgetfulness, loss, and theft. To address these shortcomings, biometric authentication is rapidly replacing traditional authentication methods and is becoming a part of everyday life. The electrocardiogram (ECG) is one of the most recent traits considered for biometric purposes. In this work we describe an ECG-based authentication system suitable for security checks and hospital environments. The proposed system will help investigators studying ECG-based biometric authentication techniques to define dataset boundaries and to acquire high-quality training data. We evaluated the performance of the proposed system and found that it could achieve up to the 92 percent identification accuracy. In addition, by applying the Amang ECG (amgecg) toolbox within MATLAB, we investigated the two parameters that directly affect the accuracy of authentication: the ECG slicing time (sliding window) and the sampling time period, and found their optimal values.

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