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5 May 2009 A model-based approach to human identification using ECG
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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.
© (2009) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Mark Homer, John M. Irvine, and Suzanne Wendelken "A model-based approach to human identification using ECG", Proc. SPIE 7306, Optics and Photonics in Global Homeland Security V and Biometric Technology for Human Identification VI, 730625 (5 May 2009); https://doi.org/10.1117/12.819327
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