A Machine Learning Framework for Biometric Authentication Using Electrocardiogram
A Machine Learning Framework for Biometric Authentication Using Electrocardiogram
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DOI：
10.1109/access.2019.2927079
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发表时间：
2019-01-01
期刊：
影响因子：
3.9
通讯作者：
Lo, Nai-Wei
中科院分区：
文献类型：
--
作者：
Kim, Song-Kyoo;Yeun, Chan Yeob;Lo, Nai-Wei
本文介绍了一个框架，如何适当地采用和调整机器学习（ML）技术用于构建基于心电图（ECG）的生物认证方案。所提出的框架可以帮助基于ECG的生物特征认证机制的研究者和开发者定义所需数据集的边界，并获得高质量的训练数据。为了确定数据集的边界，采用用例分析。基于基于ECG的身份验证的各种应用场景，开发了三种不同的用例（或身份验证类别）。随着更多的合格的训练数据提供给相应的机器学习方案，基于ML的ECG生物特征认证机制的精度也随之提高。在该框架中，使用具有R峰锚定的ECG时间切片技术来获得具有良好质量的ML训练数据。在所提出的框架中，引入了四个新的度量指标来评估ML训练和测试数据的质量。此外，还开发了一个Matlab工具箱，其中包含所有提出的机制、指标和样本数据，并使用各种ML技术进行演示，并公开供进一步研究。为了开发基于ML的ECG生物特征认证，所提出的框架可以指导研究人员准备适当的ML设置和ML训练数据集沿着三个已识别的用户案例场景。对于采用ML技术在其他研究领域设计新方案的研究人员来说，所提出的框架对于生成基于ML的高质量训练和测试数据集以及利用新的度量指标仍然是有用的。
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 are increased in consequence. The 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 the 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 the ML-based training and testing datasets with good quality and utilizing new measure metrics.