Cancelable ECG Biometrics Using Compressive Sensing-Generalized Likelihood Ratio Test
Electrocardiogram (ECG) has been investigated as promising biometrics, but it cannot be canceled and re-used once compromised just like other biometrics. We propose methods to overcome the issue of irrevocability in ECG biometrics without compromising performance. Our proposed cancelable user authen...
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doaj-c43498f7514046e6a87b163b45146e472021-03-29T22:45:32ZengIEEEIEEE Access2169-35362019-01-0179232924210.1109/ACCESS.2019.28918178606085Cancelable ECG Biometrics Using Compressive Sensing-Generalized Likelihood Ratio TestHanvit Kim0Se Young Chun1https://orcid.org/0000-0001-8739-8960Department of Electrical Engineering, Ulsan National Institute of Science and Technology, Ulsan, South KoreaDepartment of Electrical Engineering, Ulsan National Institute of Science and Technology, Ulsan, South KoreaElectrocardiogram (ECG) has been investigated as promising biometrics, but it cannot be canceled and re-used once compromised just like other biometrics. We propose methods to overcome the issue of irrevocability in ECG biometrics without compromising performance. Our proposed cancelable user authentication uses a generalized likelihood ratio test (GLRT) based on a composite hypothesis testing in compressive sensing (CS) domain. We also propose a permutation-based revocation method for CS-based cancelable biometrics so that it becomes resilient to record multiplicity attack. In addition, to compensate for inevitable performance degradation due to cancelable schemes, we also propose two performance improvement methods without undermining cancelable schemes: a self-guided ECG filtering and a T-wave shift model in our CS-GLRT. Finally, our proposed methods were evaluated for various cancelable biometrics criteria with the public ECG-ID data (89 subjects). Our cancelable ECG biometric methods yielded up to 93.0% detection probability at 2.0% false alarm ratio (PD*) and 3.8% equal error rate (EER), which are comparable to or even better than non-cancelable baseline with 93.2% PD* and 4.8% EER for challenging single-pulse ECG authentication, respectively. Our proposed methods met all cancelable biometrics criteria theoretically or empirically. Our cancelable secure user template with our novel revocation process is practically non-invertible and robust to record multiplicity attack.https://ieeexplore.ieee.org/document/8606085/Cancelable biometricsECG biometricsgeneralized likelihood ratio testcompressive sensingsingle pulse ECG |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Hanvit Kim Se Young Chun |
spellingShingle |
Hanvit Kim Se Young Chun Cancelable ECG Biometrics Using Compressive Sensing-Generalized Likelihood Ratio Test IEEE Access Cancelable biometrics ECG biometrics generalized likelihood ratio test compressive sensing single pulse ECG |
author_facet |
Hanvit Kim Se Young Chun |
author_sort |
Hanvit Kim |
title |
Cancelable ECG Biometrics Using Compressive Sensing-Generalized Likelihood Ratio Test |
title_short |
Cancelable ECG Biometrics Using Compressive Sensing-Generalized Likelihood Ratio Test |
title_full |
Cancelable ECG Biometrics Using Compressive Sensing-Generalized Likelihood Ratio Test |
title_fullStr |
Cancelable ECG Biometrics Using Compressive Sensing-Generalized Likelihood Ratio Test |
title_full_unstemmed |
Cancelable ECG Biometrics Using Compressive Sensing-Generalized Likelihood Ratio Test |
title_sort |
cancelable ecg biometrics using compressive sensing-generalized likelihood ratio test |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2019-01-01 |
description |
Electrocardiogram (ECG) has been investigated as promising biometrics, but it cannot be canceled and re-used once compromised just like other biometrics. We propose methods to overcome the issue of irrevocability in ECG biometrics without compromising performance. Our proposed cancelable user authentication uses a generalized likelihood ratio test (GLRT) based on a composite hypothesis testing in compressive sensing (CS) domain. We also propose a permutation-based revocation method for CS-based cancelable biometrics so that it becomes resilient to record multiplicity attack. In addition, to compensate for inevitable performance degradation due to cancelable schemes, we also propose two performance improvement methods without undermining cancelable schemes: a self-guided ECG filtering and a T-wave shift model in our CS-GLRT. Finally, our proposed methods were evaluated for various cancelable biometrics criteria with the public ECG-ID data (89 subjects). Our cancelable ECG biometric methods yielded up to 93.0% detection probability at 2.0% false alarm ratio (PD*) and 3.8% equal error rate (EER), which are comparable to or even better than non-cancelable baseline with 93.2% PD* and 4.8% EER for challenging single-pulse ECG authentication, respectively. Our proposed methods met all cancelable biometrics criteria theoretically or empirically. Our cancelable secure user template with our novel revocation process is practically non-invertible and robust to record multiplicity attack. |
topic |
Cancelable biometrics ECG biometrics generalized likelihood ratio test compressive sensing single pulse ECG |
url |
https://ieeexplore.ieee.org/document/8606085/ |
work_keys_str_mv |
AT hanvitkim cancelableecgbiometricsusingcompressivesensinggeneralizedlikelihoodratiotest AT seyoungchun cancelableecgbiometricsusingcompressivesensinggeneralizedlikelihoodratiotest |
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