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Proceedings Paper

A model-based approach to human identification using ECG
Author(s): Mark Homer; John M. Irvine; Suzanne Wendelken
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Paper Abstract

Biometrics, such as fingerprint, iris scan, and face recognition, offer methods for identifying individuals based on a unique physiological measurement. Recent studies indicate that a person's electrocardiogram (ECG) may also provide a unique biometric signature. Current techniques for identification using ECG rely on empirical methods for extracting features from the ECG signal. This paper presents an alternative approach based on a time-domain model of the ECG trace. Because Auto-Regressive Integrated Moving Average (ARIMA) models form a rich class of descriptors for representing the structure of periodic time series data, they are well-suited to characterizing the ECG signal. We present a method for modeling the ECG, extracting features from the model representation, and identifying individuals using these features.

Paper Details

Date Published: 5 May 2009
PDF: 10 pages
Proc. SPIE 7306, Optics and Photonics in Global Homeland Security V and Biometric Technology for Human Identification VI, 730625 (5 May 2009); doi: 10.1117/12.819327
Show Author Affiliations
Mark Homer, Charles Stark Draper Lab. (United States)
John M. Irvine, Charles Stark Draper Lab. (United States)
Suzanne Wendelken, Charles Stark Draper Lab. (United States)


Published in SPIE Proceedings Vol. 7306:
Optics and Photonics in Global Homeland Security V and Biometric Technology for Human Identification VI
B.V.K. Vijaya Kumar; Craig S. Halvorson; Šárka O. Southern; Salil Prabhakar; Arun A. Ross, Editor(s)

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