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Erschienen in: Artificial Intelligence Review 1/2021

16.09.2020

Electrocardiogram signals-based user authentication systems using soft computing techniques

verfasst von: Mehdi Hosseinzadeh, Bay Vo, Marwan Yassin Ghafour, Sajjad Naghipour

Erschienen in: Artificial Intelligence Review | Ausgabe 1/2021

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Abstract

With the advent of various security attacks, biometric authentication methods are gaining momentum in the security literature. Electrocardiogram or ECG signals are one of the essential biometric features generated by the human heart’s electrical activities. Many authentication schemes apply these signals due to their uniqueness, resistance to fabrication attacks, and support for continuous authentication. This survey article focuses on the ECG-based authentication approaches and provides the required background knowledge about the ECG signals and authentication methods. Then, it presents a taxonomy of the ECG-based authentication approaches first based on the authentication factors and then according to the applied algorithms for conducting authentication. It then describes their key contributions, applied algorithms, and possible drawbacks. Furthermore, their employed evaluation factors, ECG datasets, and simulators are illuminated and compared. Finally, the concluding remarks and future studies directions in this context are provided.

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Metadaten
Titel
Electrocardiogram signals-based user authentication systems using soft computing techniques
verfasst von
Mehdi Hosseinzadeh
Bay Vo
Marwan Yassin Ghafour
Sajjad Naghipour
Publikationsdatum
16.09.2020
Verlag
Springer Netherlands
Erschienen in
Artificial Intelligence Review / Ausgabe 1/2021
Print ISSN: 0269-2821
Elektronische ISSN: 1573-7462
DOI
https://doi.org/10.1007/s10462-020-09863-0

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