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

You are here

Inside Signal Processing Newsletter Home Page

Top Reasons to Join SPS Today!

1. IEEE Signal Processing Magazine
2. Signal Processing Digital Library*
3. Inside Signal Processing Newsletter
4. SPS Resource Center
5. Career advancement & recognition
6. Discounts on conferences and publications
7. Professional networking
8. Communities for students, young professionals, and women
9. Volunteer opportunities
10. Coming soon! PDH/CEU credits
Click here to learn more.

10 years of news and resources for members of the IEEE Signal Processing Society

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.

SPS on Twitter

  • New SPS Webinar: On Wednesday, 8 February, join Dr. Roula Nassif for "Decentralized learning over multitask graphs"…
  • CALL FOR PAPERS: IEEE Signal Processing Magazine welcomes submissions for a Special Issue on Hypercomplex Signal an…
  • New SPS Webinar: On 15 February, join Mr. Wei Liu, Dr. Li Chen and Dr. Wenyi Zhang presenting "Decentralized Federa…
  • New SPS Webinar: On Monday, 13 February, join Dr. Joe (Zhou) Ren when he presents "Human Centric Visual Analysis -…
  • Help us illustrate the SPS story! In honor of our 75th anniversary, we need your support to capture the people, mem…

SPS Videos

Signal Processing in Home Assistants


Multimedia Forensics

Careers in Signal Processing             


Under the Radar