Title
Towards a Continuous Biometric System Based on ECG Signals Acquired on the Steering Wheel.
Abstract
Electrocardiogram signals acquired through a steering wheel could be the key to seamless, highly comfortable, and continuous human recognition in driving settings. This paper focuses on the enhancement of the unprecedented lesser quality of such signals, through the combination of Savitzky-Golay and moving average filters, followed by outlier detection and removal based on normalised cross-correlation and clustering, which was able to render ensemble heartbeats of significantly higher quality. Discrete Cosine Transform (DCT) and Haar transform features were extracted and fed to decision methods based on Support Vector Machines (SVM), k-Nearest Neighbours (kNN), Multilayer Perceptrons (MLP), and Gaussian Mixture Models - Universal Background Models (GMM-UBM) classifiers, for both identification and authentication tasks. Additional techniques of user-tuned authentication and past score weighting were also studied. The method's performance was comparable to some of the best recent state-of-the-art methods (94.9% identification rate (IDR) and 2.66% authentication equal error rate (EER)), despite lesser results with scarce train data (70.9% IDR and 11.8% EER). It was concluded that the method was suitable for biometric recognition with driving electrocardiogram signals, and could, with future developments, be used on a continuous system in seamless and highly noisy settings.
Year
DOI
Venue
2017
10.3390/s17102228
SENSORS
Keywords
Field
DocType
authentication,biometrics,continuous,electrocardiogram (ECG),identification,off-the-person,outlier detection,signal denoising
Anomaly detection,Pattern recognition,Support vector machine,Word error rate,Discrete cosine transform,Speech recognition,Artificial intelligence,Engineering,Biometrics,Cluster analysis,Perceptron,Mixture model
Journal
Volume
Issue
ISSN
17
10.0
1424-8220
Citations 
PageRank 
References 
9
0.50
17
Authors
4
Name
Order
Citations
PageRank
João Ribeiro Pinto1100.86
Jaime S. Cardoso254368.74
André Lourenço331245.33
Carlos Carreiras4416.96