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Authors: Afonso Eduardo ; Helena Aidos and Ana Fred

Affiliation: Instituto de Telecomunicações and Instituto Superior Técnico, Portugal

ISBN: 978-989-758-222-6

Keyword(s): Biometrics, User Identification, Electrocardiogram (ECG), Deep Learning, Feature Learning, Transfer Learning, Deep Autoencoder.

Related Ontology Subjects/Areas/Topics: Applications ; Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Biometrics ; Biometrics and Pattern Recognition ; Cardiovascular Imaging and Cardiography ; Cardiovascular Technologies ; Computational Intelligence ; Embedding and Manifold Learning ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Methodologies and Methods ; Multimedia ; Multimedia Signal Processing ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Shape Representation ; Signal Processing ; Soft Computing ; Software Engineering ; Telecommunications ; Theory and Methods

Abstract: Biometric identification is the task of recognizing an individual using biological or behavioral traits and, recently, electrocardiogram has emerged as a prominent trait. In addition, deep learning is a fast-paced research field where several models, training schemes and applications are being actively investigated. In this paper, an ECG-based biometric system using a deep autoencoder to learn a lower dimensional representation of heartbeat templates is proposed. A superior identification performance is achieved, validating the expressiveness of such representation. A transfer learning setting is also explored and results show practically no loss of performance, suggesting that these deep learning methods can be deployed in systems with offline training.

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Paper citation in several formats:
Eduardo A., Aidos H. and Fred A. (2017). ECG-based Biometrics using a Deep Autoencoder for Feature Learning - An Empirical Study on Transferability.In Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-222-6, pages 463-470. DOI: 10.5220/0006195404630470

@conference{icpram17,
author={Afonso Eduardo and Helena Aidos and Ana Fred},
title={ECG-based Biometrics using a Deep Autoencoder for Feature Learning - An Empirical Study on Transferability},
booktitle={Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2017},
pages={463-470},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006195404630470},
isbn={978-989-758-222-6},
}

TY - CONF

JO - Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - ECG-based Biometrics using a Deep Autoencoder for Feature Learning - An Empirical Study on Transferability
SN - 978-989-758-222-6
AU - Eduardo A.
AU - Aidos H.
AU - Fred A.
PY - 2017
SP - 463
EP - 470
DO - 10.5220/0006195404630470

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