ECG biometric recognition: Review, system proposal, and benchmark evaluation
Entity
UAM. Departamento de Tecnología Electrónica y de las ComunicacionesPublisher
IEEEDate
2023-02-13Citation
10.1109/ACCESS.2023.3244651
IEEE Access 11 (2023): 15555-15566
ISSN
2169-3536DOI
10.1109/ACCESS.2023.3244651Funded by
This work was supported in part by the European Union’s Horizon 2020 Research and Innovation Programme under the Marie Sklodowska-Curie Grant under Agreement 860813-TRESPASS-ETN and Agreement 860315-PRIMA-ITN; in part by the IDEA-FAST under Grant IMI2-2018-15-853981; and in part by INTER-ACTION, Ministerio de Ciencia e Innovación (MICINN)/Fondo Europeo de Desarrollo Regional (FEDER), under Grant PID2021-126521OB-I00Project
info:eu-repo/grantAgreement/EC/H2020/860813; info:eu-repo/grantAgreement/EC/H2020/860315; info:eu-repo/grantAgreement/EC/H2020/853981; Gobierno de España. PID2021-126521OB-I00Editor's Version
https://doi.org/10.1109/ACCESS.2023.3244651Subjects
Biometrics; deep learning; ECG; recognition; verification; TelecomunicacionesAbstract
ECGs have shown unique patterns to distinguish between different subjects and present important advantages compared to other biometric traits. However, the lack of public data and standard experimental protocols makes the evaluation and comparison of novel ECG methods difficult. In this study, we perform extensive analysis and comparison of different scenarios in ECG biometric recognition. We consider verification and identification tasks, single- and multi-session settings, and single- and multi-lead ECGs recorded with traditional and user-friendly devices. We also present ECGXtractor, a robust Deep Learning technology trained with an in-house large-scale database, and evaluate it with detailed experimental protocol and public databases. With the popular PTB database, we achieve Equal Error Rates of 0.14% and 2.06% in single- and multi-session verification. The results achieved prove the soundness of ECGXtractor across multiple scenarios and databases. We release the source code, experimental protocol details, and pre-trained models in GitHub to advance in the field
Files in this item
Google Scholar:Melzi, Pietro
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Tolosana Moranchel, Rubén
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Vera Rodríguez, Rubén
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