Advertisement

Cognitive Biometrics: Challenges for the Future

  • Kenneth Revett
  • Sergio Tenreiro de Magalhães
Part of the Communications in Computer and Information Science book series (CCIS, volume 92)

Abstract

Cognitive biometrics is a novel approach to user authentication/identification which utilises a biosignal based approach. Specifically, current implementations rely on the use of the electroencephalogram (EEG), electrocardiogram (ECG), and the electrodermal response (EDR) as inputs into a traditional authentication scheme. The scientific basis for the deployment of biosignals resides principally on their uniqueness -for instance the theta power band in adults presents a phenotypic/genetic correlation of approximately 75%. The numbers are roughly the same for ECG, with an heritability correlation for the peak-to-peak (R-R interval) times of over 77%. For EDR, the results indicate that there is approximately a 50% heritability score (h2). The challenge with respect to cognitive biometrics based on biosignals is to enhance the information content of the acquired data.

Keywords

Cognitive biometrics EEG ECG EDR user authentication 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Forsen, G., Nelson, M., Staron, R.: Personal attributes authentication techniques. In: Griffin, A.F.B. (ed.) Rome Air Development Center report RADC-TR-77-1033. RADC, New York (1977)Google Scholar
  2. 2.
    Waller, A.D.: A demonstration on man of electromotive changes accompanying the heart’s beat. J. Physiol (Lond.) 8, 229–234 (1887)Google Scholar
  3. 3.
    Silva, H., Gamboa, H., Fred, A.: Applicability of lead V2 ECG Measurements in Biometrics. In: Proceedings of Med-e-Tel 2007, Luxembourg (April 2007)Google Scholar
  4. 4.
    Israel, S., Irvine, J., Cheng, A., Wiederhold, M., Wiederhold, B.: ECG to identify individuals. Pattern Recognition 38(1), 133–142 (2005)CrossRefGoogle Scholar
  5. 5.
    Biel, L., Petterson, O., Stork, D.: ECG analysis: a new approach in human identifiaction. IEE Transactions on Instrumentation and Measurement 50(3), 808–812 (2001)CrossRefGoogle Scholar
  6. 6.
    Kyoso, M., Uchiyama, A.: Development of an ECG identifcation system. In: Proceedings of the 23rd Annual International IEEE Conference on Engineering in Medicine and Biology Society, Instanbul, Turkey, pp. 3721–3723 (2001)Google Scholar
  7. 7.
    Kyeong-Seop, K., Tae-Ho, Y., Jeong-Whan, L., Dong-Jun, K., Heung-Seo, K.: A Robust Human Identification by Normalized Time-Domain Features of Electrocardiogram. In: IEEE EMBS 27th Annual Uinternational Conference of Engineering in Medicine and Biology (EMBS 2005), pp. 1114–1117 (2005)Google Scholar
  8. 8.
    Mehta, S.S., Lingayat, N.S.: Comparative study of QRS detection in single lead and 12 lead ECG based on entropy and combined entropy criteria using support vector machine. Journal of Theoretical and Applied Information Technology, 8–18 (2007)Google Scholar
  9. 9.
    Vidal, J.: Toward direct brain-computer communication. Annual Review Biophys. Bioeng., 157–180 (1973)Google Scholar
  10. 10.
    Palaniappan, R.: Multiple mental thought parmetric classification: a new approach for individual identification. International Journal of Signal processing 2(1), 222–225 (2005)Google Scholar
  11. 11.
    Marcel, S., del Millan, R.: Person autentication using brainwaves (EEG) and maximum a posteriori model adaptation. IEEE transcations on pattern Analysis and machine Intelligence, Special issue on Biometrics (2006)Google Scholar
  12. 12.
    Riera, A., Soria-Frisch, A., Caparrini, M., Grau, C., Ruffini, G.: Unobtrusive biometric system based on electroencephalogram analysis. EURASIP Journal on Advances in Signal Processing (2007)Google Scholar
  13. 13.
    Bell, C.J., Shenoy, P., Chalodhorn, R., Rao, R.P.N.: An image-based brain-computer interface using the P3 response, UWCSE Tech. Report # 2007-02-03, University of Washington, Seattle, Washington, USA (2007)Google Scholar
  14. 14.
    Riera, A., Soria-Frisch, A., Caparrini, M., Grau, C., Ruffini, G.: Unobtrusive biometric system based on electroencephalogram analysis. EURASIP Journal on Advances in Signal Processing (2007)Google Scholar
  15. 15.
    Delourme, A., Makeig, S.: EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. Journal of neuroscience Methods 134, 9–21 (2004)CrossRefGoogle Scholar
  16. 16.
    Sutton, S., Braren, M., Zubin, J., John, E.R.: Evoked potential correlates of stimulus uncertainty. Science 150, 1187–1188 (1965)CrossRefGoogle Scholar
  17. 17.
    Nykopp, T.: Statistical modelling issues for the adaptive brain interface, MSc Thesis, Helsinki Univeristy of Technology (2001)Google Scholar
  18. 18.
    Gupta, C.N., Palaniappan, R.: Enhanced detection of visual-evoked potentials in brain-computer interface using gentic algorithm and cyclostationary analysis. Computational Intelligence in Neuroscience (2007)Google Scholar
  19. 19.
    Thorpe, J., Van Oorschot, P.C.: Graphical Dictionaries and the Memorable Space of Graphical Passwords. In: Proceedings of the 13th USENIX Security Symposium, pp. 135–150 (2004)Google Scholar
  20. 20.
    Paranjape, R.B., Mahovsky, J., Benedicenti, L., Kolesapos, Z.: The lectroencephalogram as a biometric. In: Canadian Conference on Electrical and Computer Engineering, pp. 1363–1366 (2001)Google Scholar
  21. 21.
    Polous, M., Rangoussi, M., Alexandris, N.: Neural network based person identification using EEG features. In: Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal processing, vol. 2, pp. 1117–1120 (1999)Google Scholar
  22. 22.
    Crider, A., Kremen, W.S., Xian, H., Jacobson, K.C., Waterman, B., Eisen, S.A., Tsuang, M.T., Lyons, M.J.: Stability, consistency, and heritability of electrodermal response lability in middle-aged male twins. Psychophysiology 41(4), 501–509 (2004)Google Scholar
  23. 23.
    Silva, D.C., Vinhas, V., Reis, L.P., Oliviera, E.: Biometric emotion assessment and feedback in an immersive digitial environment. Int. J. Soc. Robots 1, 307–317 (2009)CrossRefGoogle Scholar
  24. 24.
    Russell, J.A.: A circumplex model of affect. J. Personal Soc. Psychol. 39, 1161–1170 (1980)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Kenneth Revett
    • 1
  • Sergio Tenreiro de Magalhães
    • 2
  1. 1.School of Electronics & Computer Science LondonUniversity of WestminsterEngland
  2. 2.Universidade Catolica PortuguesaBraga

Personalised recommendations