A Machine Learning Framework for Biometric Authentication using Electrocardiogram

TL;DR

This paper presents a comprehensive framework for developing ECG-based biometric authentication using machine learning, including dataset preparation, quality metrics, and a MATLAB toolbox for researchers.

Contribution

It introduces a novel framework with quality metrics and a MATLAB toolbox to guide the development of ML-based ECG biometric authentication schemes.

Findings

Framework improves accuracy of ECG biometric authentication.

Four new metrics evaluate training and testing data quality.

Public MATLAB toolbox facilitates further research.

Abstract

This paper introduces a framework for how to appropriately adopt and adjust Machine Learning (ML) techniques used to construct Electrocardiogram (ECG) based biometric authentication schemes. The proposed framework can help investigators and developers on ECG based biometric authentication mechanisms define the boundaries of required datasets and get training data with good quality. To determine the boundaries of datasets, use case analysis is adopted. Based on various application scenarios on ECG based authentication, three distinct use cases (or authentication categories) are developed. With more qualified training data given to corresponding machine learning schemes, the precision on ML-based ECG biometric authentication mechanisms is increased in consequence. ECG time slicing technique with the R-peak anchoring is utilized in this framework to acquire ML training data with good quality. In the proposed framework four new measure metrics are introduced to evaluate the quality of ML training and testing data. In addition, a Matlab toolbox, containing all proposed mechanisms, metrics and sample data with demonstrations using various ML techniques, is developed and made publicly available for further investigation. For developing ML-based ECG biometric authentication, the proposed framework can guide researchers to prepare the proper ML setups and the ML training datasets along with three identified user case scenarios. For researchers adopting ML techniques to design new schemes in other research domains, the proposed framework is still useful for generating ML-based training and testing datasets with good quality and utilizing new measure metrics.