Finger ECG signal for user authentication: Usability and performance

Over the past few years, the evaluation of Electrocardio-graphic (ECG) signals as a prospective biometric modality has revealed promising results. Given the vital and continuous nature of this information source, ECG signals offer several advantages to the field of biometrics; yet, several challenges currently prevent the ECG from being adopted as a biometric modality in operational settings. These arise partially due to ECG signal's clinical tradition and intru-siveness, but also from the lack of evidence on the permanence of the ECG templates over time. The problem of in-trusiveness has been recently overcome with the “off-the-person” approach for capturing ECG signals. In this paper we provide an evaluation of the permanence of ECG signals collected at the fingers, with respect to the biometric authentication performance. Our experimental results on a small dataset suggest that further research is necessary to account for and understand sources of variability found in some subjects. Despite these limitations, “off-the-person” ECG appears to be a viable trait for multi-biometric or standalone biometrics, low user throughput, real-world scenarios.

[1]  Adrian D. C. Chan,et al.  Wavelet Distance Measure for Person Identification Using Electrocardiograms , 2008, IEEE Transactions on Instrumentation and Measurement.

[2]  Dimitrios Hatzinakos,et al.  Analysis of Human Electrocardiogram for Biometric Recognition , 2008, EURASIP J. Adv. Signal Process..

[3]  Jeffrey M. Hausdorff,et al.  Physionet: Components of a New Research Resource for Complex Physiologic Signals". Circu-lation Vol , 2000 .

[4]  Ognian Boumbarov,et al.  ECG personal identification in subspaces using radial basis neural networks , 2009, 2009 IEEE International Workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications.

[5]  Brenda K. Wiederhold,et al.  ECG to identify individuals , 2005, Pattern Recognit..

[6]  P. Gupta,et al.  ECG to Individual Identification , 2008, 2008 IEEE Second International Conference on Biometrics: Theory, Applications and Systems.

[7]  A. Uchiyama,et al.  Development of an ECG identification system , 2001, 2001 Conference Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[8]  Sanjay Kumar Singh,et al.  Evaluation of Electrocardiogram for Biometric Authentication , 2012, J. Information Security.

[9]  A. L. N. Fred,et al.  Applicability of Lead V2 ECG Measurements in Biometrics , 2007 .

[10]  Joseph A. O'Sullivan,et al.  ECG Biometric Recognition: A Comparative Analysis , 2012, IEEE Transactions on Information Forensics and Security.

[11]  Sharath Pankanti,et al.  Biometrics: Personal Identification in Networked Society , 2013 .

[12]  Willis J. Tompkins,et al.  Implementation of a one-lead ECG human identification system on a normal population , 2010 .

[13]  Dimitrios Hatzinakos,et al.  Heart Biometrics: Theory, Methods and Applications , 2011 .

[14]  Ana L. N. Fred,et al.  Unveiling the Biometric Potential of Finger-Based ECG Signals , 2011, Comput. Intell. Neurosci..

[15]  L. Biel,et al.  ECG analysis: a new approach in human identification , 1999, IMTC/99. Proceedings of the 16th IEEE Instrumentation and Measurement Technology Conference (Cat. No.99CH36309).

[16]  Yu Hen Hu,et al.  One-lead ECG for identity verification , 2002, Proceedings of the Second Joint 24th Annual Conference and the Annual Fall Meeting of the Biomedical Engineering Society] [Engineering in Medicine and Biology.

[17]  Robert Plonsey,et al.  Bioelectromagnetism: Principles and Applications of Bioelectric and Biomagnetic Fields , 1995 .

[18]  Ana L. N. Fred,et al.  Clinical Data Privacy and Customization via Biometrics Based on ECG Signals , 2011, USAB.

[19]  Ana L. N. Fred,et al.  One-Lead ECG-based Personal Identification Using Ziv-Merhav Cross Parsing , 2010, 2010 20th International Conference on Pattern Recognition.

[20]  Chih-Yu Hsu,et al.  A Novel Personal Identity Verification Approach Using a Discrete Wavelet Transform of the ECG Signal , 2008, 2008 International Conference on Multimedia and Ubiquitous Engineering (mue 2008).

[21]  Hugo Silva,et al.  Study and evaluation of a single differential sensor design based on electro-textile electrodes for ECG biometrics applications , 2011, 2011 IEEE SENSORS Proceedings.

[22]  Ana L. N. Fred,et al.  ECG-based biometrics: A real time classification approach , 2012, 2012 IEEE International Workshop on Machine Learning for Signal Processing.

[23]  Hany Selim,et al.  Human identification using time normalized QT signal and the QRS complex of the ECG , 2010, 2010 7th International Symposium on Communication Systems, Networks & Digital Signal Processing (CSNDSP 2010).

[24]  Neil Yager,et al.  The Biometric Menagerie , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[25]  Dimitrios Hatzinakos,et al.  Securing handheld devices and fingerprint readers with ECG biometrics , 2012, 2012 IEEE Fifth International Conference on Biometrics: Theory, Applications and Systems (BTAS).

[26]  Ana L. N. Fred,et al.  ECG Biometrics: Principles and Applications , 2013, BIOSIGNALS.