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

Eduardo, A. ; Aidos, H. ; Fred, A. L. N.

ECG-based Biometrics using a Deep Autoencoder for Feature Learning: An Empirical Study on Transferability, Proc INSTICC International Conf. on Pattern Recognition Applications and Methods - ICPRAM, Porto, Portugal, Vol. 1, pp. 463 - 470, February, 2017.

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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.