Su Bıcakcı, Hazal, Santopietro, Marco, Boakes, Matthew, Guest, Richard (2022) Evaluation of Electrocardiogram Biometric Verification Models Based on Short Enrollment Time on Medical and Wearable Recorders. In: 2021 International Carnahan Conference on Security Technology (ICCST). . IEEE ISBN 978-1-66549-989-7. E-ISBN 978-1-66549-988-0. (doi:10.1109/ICCST49569.2021.9717372) (KAR id:94087)
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Official URL: https://doi.org/10.1109/ICCST49569.2021.9717372 |
Abstract
Biometric authentication is nowadays widely used in a multitude of scenarios. Several studies have been conducted on electrocardiogram (ECG) for subject identification or verification among the various modalities. However, none have considered a typical implementation with a mobile device and the necessity for
a fast-training model with limited recording time for the signal. This study tackles this issue by exploring various classification models on short recordings and evaluating the performance varying the sample length and the training set size. We run our tests on two public datasets collected from wearable and medical
devices and propose a pipeline for ECG authentication with limited data required for competitive usage across applications.
Item Type: | Conference or workshop item (Paper) |
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DOI/Identification number: | 10.1109/ICCST49569.2021.9717372 |
Uncontrolled keywords: | Biometric Authentication, ECG Biometrics, Performance Assessment, Wearable devices |
Subjects: | Q Science > QA Mathematics (inc Computing science) |
Divisions: | Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Engineering and Digital Arts |
Depositing User: | Richard Guest |
Date Deposited: | 20 Apr 2022 13:05 UTC |
Last Modified: | 10 Jan 2024 17:04 UTC |
Resource URI: | https://kar.kent.ac.uk/id/eprint/94087 (The current URI for this page, for reference purposes) |
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