Autor(es):
Belo, David ; Bento, Nuno ; Silva, Hugo ; Fred, Ana ; Gamboa, Hugo
Data: 2020
Identificador Persistente: http://hdl.handle.net/10362/114527
Origem: Repositório Institucional da UNL
Assunto(s): Artificial neural networks; Authentication; Biometrics; Biosignal; Convolutional neural network; Deep learning; Electrocardiogram; Identification; Recurrent neural network; RLTC; Analytical Chemistry; Biochemistry; Atomic and Molecular Physics, and Optics; Instrumentation; Electrical and Electronic Engineering
Descrição
PD/BDE/130216/2017
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.
