Authors:
Afonso Eduardo
;
Helena Aidos
and
Ana Fred
Affiliation:
Instituto de Telecomunicações and Instituto Superior Técnico, Portugal
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.