An Enhanced Electrocardiogram Biometric Authentication System Using Machine Learning

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 a...

Full description

Saved in:
Bibliographic Details
Published in:arXiv.org 2019-09
Main Authors: Ebrahim Al Alkeem, Song-Kyoo, Kim, Chan Yeob Yeun, Zemerly, M Jamal, Poon, Kin, Yoo, Paul D
Format: Article
Language:English
Subjects:
Authentication
Biometrics
Electrocardiography
Machine learning
Sampling
Security
Security clearances
Slicing
Sliding
Theft
Wearable technology
Windows (intervals)
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2331-8422
DOI:10.48550/arxiv.1907.00366