ECG-based Biometrics using a Deep Autoencoder for Feature Learning - An Empirical Study on Transferability

Afonso Eduardo, Helena Aidos, Ana Fred

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 Harvard Style

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


in Bibtex Style

@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},
}


in EndNote Style

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