Foteini Agrafioti (University of Toronto), “ECG in Biometric Recognition: Time Dependency and Application Challenges” (2011)

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Foteini Agrafioti (University of Toronto), “ECG in Biometric Recognition: Time Dependency and Application Challenges” (2011)

Foteini  Agrafioti (University of Toronto), “ECG in Biometric Recognition: Time Dependency and Application Challenges”, Advisor: Dimitrios Hatzinakos (2011)

As biometric recognition becomes increasingly popular, the fear of circumvention, obfuscation and replay attacks is a rising concern. Traditional biometric modalities such as the face, the fingerprint or the iris are vulnerable to such attacks, which defeats the purpose of biometric recognition.

This thesis advocates the use the electrocardiogram (ECG) signal for human identity recognition. The ECG is a vital signal of the human body, and as such, it naturally provides liveness detection, robustness to attacks, universality and permanence. In addition, ECG inherently satisfies uniqueness requirements, because the morphology of the signal is highly dependent on the particular anatomical and geometrical characteristics of the myocardium in the heart. However, the ECG is a continuous signal, and this presents a great challenge to biometric recognition. With this modality, instantaneous variability is expected even within recordings of the same individual due to a variety of factors, including recording noise, or physical and psychological activity. While the noise and heart rate variations due to physical exercise can be addressed with appropriate feature extraction, the effects of emotional activity on the ECG signal are more obscure.

This thesis deals with this problem from an affective computing point of view. This thesis attempts to provide the necessary algorithmic and practical framework for the real-life deployment of the ECG signal in biometric recognition.

For details, please contact the author or visit the thesis page.

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