Department of Electrical and Computer Engineering, Portland State University, Portland, OR 97201, USA
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Author to whom correspondence should be addressed.
Academic Editor: Panicos Kyriacou
Received: 4 December 2016 / Revised: 17 January 2017 / Accepted: 9 February 2017 / Published: 20 February 2017
(This article belongs to the Section Biosensors)
Abstract
Electrocardiogram (ECG) signals sensed from mobile devices pertain the potential for biometric identity recognition applicable in remote access control systems where enhanced data security is demanding. In this study, we propose a new algorithm that consists of a two-stage classifier combining random forest and wavelet distance measure through a probabilistic threshold schema, to improve the effectiveness and robustness of a biometric recognition system using ECG data acquired from a biosensor integrated into mobile devices. The proposed algorithm is evaluated using a mixed dataset from 184 subjects under different health conditions. The proposed two-stage classifier achieves a total of 99.52% subject verification accuracy, better than the 98.33% accuracy from random forest alone and 96.31% accuracy from wavelet distance measure algorithm alone. These results demonstrate the superiority of the proposed algorithm for biometric identification, hence supporting its practicality in areas such as cloud data security, cyber-security or remote healthcare systems. View Full-Text
Keywords: electrocardiogram (ECG); biometric recognition; random forest; wavelet distance measure; data security
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