ECG Biometric Recognition: Review, System Proposal, and Benchmark Evaluation-Reference-Cited by-同舟云学术

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ECG Biometric Recognition: Review, System Proposal, and Benchmark Evaluation

Published:2023 Issue: Volume:11 Page:15555-15566
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Melzi Pietro¹^ORCID,Tolosana Ruben¹^ORCID,Vera-Rodriguez Ruben¹^ORCID

Affiliation:

1. Biometrics and Data Pattern Analytics (BiDA) Laboratory, Universidad Autonoma de Madrid, Madrid, Spain

Funder

European Union’s Horizon 2020 Research and Innovation Programme

Marie Sklodowska-Curie

IDEA-FAST

INTER-ACTION, Ministerio de Ciencia e Innovación (MICINN)/Fondo Europeo de Desarrollo Regional

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/10005208/10043674.pdf?arnumber=10043674

Reference53 articles.

1. Handbook of Digital Face Manipulation and Detection

2. Presentation attacks in signature biometrics: Types and introduction to attack detection;tolosana;Handbook of Biometric Anti-spoofing,2018

3. Analyzing artificial intelligence systems for the prediction of atrial fibrillation from sinus-rhythm ECGs including demographics and feature visualization

4. On the vulnerability of ECG verification to online presentation attacks

5. A Survey of Privacy Vulnerabilities of Mobile Device Sensors

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2. A Task-Generic High-Performance Unsupervised Pre-Training Framework for ECG;IEEE Sensors Journal;2024-07-01

3. Federated Learning and Differential Privacy Techniques on Multi-hospital Population-scale Electrocardiogram Data;Proceedings of the 2024 8th International Conference on Medical and Health Informatics;2024-05-17

4. AI-Driven Scalable Authentication Framework Using ECG and EEG Biometrics for Enhanced Digital Security;2024 15th Annual Undergraduate Research Conference on Applied Computing (URC);2024-04-24

5. HGSSA-bi LSTM: A Secure Multimodal Biometric Sensing Using Optimized Bi-Directional Long Short-Term Memory with Self-Attention;ECS Sensors Plus;2024-01-24

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