ECG Biometrics Using Deep Learning and Relative Score Threshold Classification
The field of biometrics is a pattern recognition problem, where the individual traits are coded, registered, and compared with other database records. Due to the difficulties in reproducing Electrocardiograms (ECG), their usage has been emerging in the biometric field for more secure applications. I...
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doaj-883c220a7bfe4fe3a7707a4b9081ac8a2020-11-25T03:14:46ZengMDPI AGSensors1424-82202020-07-01204078407810.3390/s20154078ECG Biometrics Using Deep Learning and Relative Score Threshold ClassificationDavid Belo0Nuno Bento1Hugo Silva2Ana Fred3Hugo Gamboa4LIBPhys, Physics Department, Faculty of Sciences and Technology, Nova University of Lisbon, 2825-149 Caparica, PortugalLIBPhys, Physics Department, Faculty of Sciences and Technology, Nova University of Lisbon, 2825-149 Caparica, PortugalInstituto de Telecomunicacoes, Instituto Superior Tecnico (IST), Technical University of Lisbon, 1049-001 Lisboa, PortugalInstituto de Telecomunicacoes, Instituto Superior Tecnico (IST), Technical University of Lisbon, 1049-001 Lisboa, PortugalLIBPhys, Physics Department, Faculty of Sciences and Technology, Nova University of Lisbon, 2825-149 Caparica, PortugalThe field of biometrics is a pattern recognition problem, where the individual traits are coded, registered, and compared with other database records. Due to the difficulties in reproducing Electrocardiograms (ECG), their usage has been emerging in the biometric field for more secure applications. Inspired by the high performance shown by Deep Neural Networks (DNN) and to mitigate the intra-variability challenges displayed by the ECG of each individual, this work proposes two architectures to improve current results in both identification (finding the registered person from a sample) and authentication (prove that the person is whom it claims) processes: Temporal Convolutional Neural Network (TCNN) and Recurrent Neural Network (RNN). Each architecture produces a similarity score, based on the prediction error of the former and the logits given by the last, and fed to the same classifier, the Relative Score Threshold Classifier (RSTC).The robustness and applicability of these architectures were trained and tested on public databases used by literature in this context: Fantasia, MIT-BIH, and CYBHi databases. Results show that overall the TCNN outperforms the RNN achieving almost 100%, 96%, and 90% accuracy, respectively, for identification and 0.0%, 0.1%, and 2.2% equal error rate (EER) for authentication processes. When comparing to previous work, both architectures reached results beyond the state-of-the-art. Nevertheless, the improvement of these techniques, such as enriching training with extra varied data and transfer learning, may provide more robust systems with a reduced time required for validation.https://www.mdpi.com/1424-8220/20/15/4078deep learningbiometricselectrocardiogramconvolutional neural networkrecurrent neural networkauthentication |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
David Belo Nuno Bento Hugo Silva Ana Fred Hugo Gamboa |
spellingShingle |
David Belo Nuno Bento Hugo Silva Ana Fred Hugo Gamboa ECG Biometrics Using Deep Learning and Relative Score Threshold Classification Sensors deep learning biometrics electrocardiogram convolutional neural network recurrent neural network authentication |
author_facet |
David Belo Nuno Bento Hugo Silva Ana Fred Hugo Gamboa |
author_sort |
David Belo |
title |
ECG Biometrics Using Deep Learning and Relative Score Threshold Classification |
title_short |
ECG Biometrics Using Deep Learning and Relative Score Threshold Classification |
title_full |
ECG Biometrics Using Deep Learning and Relative Score Threshold Classification |
title_fullStr |
ECG Biometrics Using Deep Learning and Relative Score Threshold Classification |
title_full_unstemmed |
ECG Biometrics Using Deep Learning and Relative Score Threshold Classification |
title_sort |
ecg biometrics using deep learning and relative score threshold classification |
publisher |
MDPI AG |
series |
Sensors |
issn |
1424-8220 |
publishDate |
2020-07-01 |
description |
The field of biometrics is a pattern recognition problem, where the individual traits are coded, registered, and compared with other database records. Due to the difficulties in reproducing Electrocardiograms (ECG), their usage has been emerging in the biometric field for more secure applications. Inspired by the high performance shown by Deep Neural Networks (DNN) and to mitigate the intra-variability challenges displayed by the ECG of each individual, this work proposes two architectures to improve current results in both identification (finding the registered person from a sample) and authentication (prove that the person is whom it claims) processes: Temporal Convolutional Neural Network (TCNN) and Recurrent Neural Network (RNN). Each architecture produces a similarity score, based on the prediction error of the former and the logits given by the last, and fed to the same classifier, the Relative Score Threshold Classifier (RSTC).The robustness and applicability of these architectures were trained and tested on public databases used by literature in this context: Fantasia, MIT-BIH, and CYBHi databases. Results show that overall the TCNN outperforms the RNN achieving almost 100%, 96%, and 90% accuracy, respectively, for identification and 0.0%, 0.1%, and 2.2% equal error rate (EER) for authentication processes. When comparing to previous work, both architectures reached results beyond the state-of-the-art. Nevertheless, the improvement of these techniques, such as enriching training with extra varied data and transfer learning, may provide more robust systems with a reduced time required for validation. |
topic |
deep learning biometrics electrocardiogram convolutional neural network recurrent neural network authentication |
url |
https://www.mdpi.com/1424-8220/20/15/4078 |
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