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This article is part of the series Advanced Signal Processing and Pattern Recognition Methods for Biometrics.

Open Access Research Article

Analysis of Human Electrocardiogram for Biometric Recognition

Yongjin Wang*, Foteini Agrafioti, Dimitrios Hatzinakos and Konstantinos N Plataniotis

Author Affiliations

The Edward S. Rogers Sr., Department of Electrical and Computer Engineering, University of Toronto, 10 King's College Road, Toronto, ON, Canada, M5S 3G4

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EURASIP Journal on Advances in Signal Processing 2008, 2008:148658  doi:10.1155/2008/148658


The electronic version of this article is the complete one and can be found online at: http://asp.eurasipjournals.com/content/2008/1/148658


Received:3 May 2007
Accepted:30 August 2007
Published:19 September 2007

© 2008 The Author(s).

This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Security concerns increase as the technology for falsification advances. There are strong evidences that a difficult to falsify biometric trait, the human heartbeat, can be used for identity recognition. Existing solutions for biometric recognition from electrocardiogram (ECG) signals are based on temporal and amplitude distances between detected fiducial points. Such methods rely heavily on the accuracy of fiducial detection, which is still an open problem due to the difficulty in exact localization of wave boundaries. This paper presents a systematic analysis for human identification from ECG data. A fiducial-detection-based framework that incorporates analytic and appearance attributes is first introduced. The appearance-based approach needs detection of one fiducial point only. Further, to completely relax the detection of fiducial points, a new approach based on autocorrelation (AC) in conjunction with discrete cosine transform (DCT) is proposed. Experimentation demonstrates that the AC/DCT method produces comparable recognition accuracy with the fiducial-detection-based approach.

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