Date of Award

9-1-2009

Document Type

Thesis (Master's)

Department

Department of Computer Science

First Advisor

Tanzeem Choudhury

Abstract

Identity fraud due to lost, stolen or shared information or tokens that represent an individual's identity is becoming a growing security concern. Biometric recognition - the identification or verification of claimed identity, shows great potential in bridging some of the existing security gaps. It has been shown that the human Electrocardiogram (ECG) exhibits sufficiently unique patterns for use in biometric recognition. But it also exhibits significant variability due to stress or activity, and signal artifacts due to movement. In this thesis, we develop a novel activity-aware ECG-based biometric recognition scheme that can verify/identify under different activity conditions. From a pattern recognition standpoint, we develop algorithms for preprocessing, feature extraction and probabilistic classification. We pay particular attention to the applicability of the proposed scheme in ongoing biometric verification of claimed identity. Finally we propose a wearable prototype architecture of our scheme.

Comments

Originally posted in the Dartmouth College Computer Science Technical Report Series, number TR2009-655.

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