CardioID

Mitigating the Effects of Irregular Cardiac Signals for Biometric Identification

Journal Article (2022)
Author(s)

Weizheng Wang (TU Delft - Embedded Systems)

Qing Wang (TU Delft - Embedded Systems)

Marco Zúñiga Zuñiga Zamalloa (TU Delft - Embedded Systems)

Research Group
Embedded Systems
Copyright
© 2022 Weizheng Wang, Q. Wang, Marco Zuniga
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Publication Year
2022
Language
English
Copyright
© 2022 Weizheng Wang, Q. Wang, Marco Zuniga
Research Group
Embedded Systems
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Abstract

Cardiac patterns are being used to obtain hard-to-forge biometric signatures and have led to high accuracy in state-of-the-art (SoA) identification applications. However, this performance is obtained under controlled scenarios where cardiac signals maintain a relatively uniform pattern, facilitating the identification process. In this work, we analyze cardiac signals collected in more realistic (uncontrolled) scenarios and show that their high signal variability (i.e., ir-regularity) makes it harder to obtain stable and distinct user features. Furthermore, SoA usually fails to identify specific groups of users, rendering existing identification methods futile in uncontrolled scenarios. To solve these problems, we propose a framework with three novel properties. First, we design an adaptive method that achieves stable and distinct features by tailoring the filtering spectrum to each user. Second, we show that users can have multiple cardiac morpholo-gies, offering us a much bigger pool of cardiac signals and users compared to SoA. Third, we overcome other distortion effects present in authentication applications with a multi-cluster approach and the Mahalanobis distance. Our evaluation shows that the average balanced accuracy (BAC) of SoA drops from above 90% in controlled scenarios to 75% in uncontrolled ones, while our method maintains an average BAC above 90% in uncontrolled scenarios.

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