Robustness of Biometric Gait Authentication Against Impersonation Attack

  • Davrondzhon Gafurov
  • Einar Snekkenes
  • Tor Erik Buvarp
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4277)


This paper presents a gait authentication based on time-normalized gait cycles. Unlike most of the previous works in gait recognition, using machine vision techniques, in our approach gait patterns are obtained from a physical sensor attached to the hip. Acceleration in 3 directions: up-down, forward-backward and sideways of the hip movement, which is obtained by the sensor, is used for authentication. Furthermore, we also present a study on the security strength of gait biometric against imitating or mimicking attacks, which has not been addressed in biometric gait recognition so far.


Gait Pattern Impersonation Attack Biometric System False Reject Rate Gait Recognition 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Hayfron-Acquah, J.B., Nixon, M.S., Carter, J.N.: Automatic gait recognition by symmetry analysis. In: Audio- and Video-Based Biometric Person Authentication, pp. 272–277 (2001)Google Scholar
  2. 2.
    Shutler, J., Nixon, M.: Zernike velocity moments for description and recognition of moving shapes. In: British Machine Vision Conference, pp. 11/1–11/4 (2001) Session 8: Modelling BehaviourGoogle Scholar
  3. 3.
    Cunado, D., Nixon, M., Carter, J.: Automatic extraction and description of human gait models for recognition purposes. In: Computer Vision and Image Understanding, pp. 1–41 (2003)Google Scholar
  4. 4.
    Wang, L., Hu, W., Tan, T.: A new attempt to gait-based human identification. In: International Conference on Pattern Recognition, pp. 115–118 (2002)Google Scholar
  5. 5.
    BenAbdelkader, C., Cutler, R., Davis, L.: Stride and cadence as a biometric in automatic person identification and verification. In: Fifth IEEE International Conference on Automatic Face and Gesture Recognition, May 2002, pp. 357–362 (2002)Google Scholar
  6. 6.
    BenAbdelkader, C., Cutler, R., Nanda, H., Davis, L.: Eigengait: Motion-based recognition of people using image self-similarity. In: Audio-and Video-Based Biometric Person Authentication (2001)Google Scholar
  7. 7.
    Johnson, A.Y., Bobick, A.F.: A multi-view method for gait recognition using static body parameters. In: Third International Conference on Audio- and Video-Based Biometric Person Authentication, June 2001, pp. 301–311 (2001)Google Scholar
  8. 8.
    Sarkar, S., Phillips, P.J., Liu, Z., Vega, I.R., Grother, P., Bowyer, K.W.: The humanid gait challenge problem: Data sets, performance, and analysis. IEEE Trans. Pattern Anal. Mach. Intell. 27(2), 162–177 (2005)CrossRefGoogle Scholar
  9. 9.
    Lam, T.H.W., Lee, R.S.T.: A new representation for human gait recognition: Motion silhouettes image (msi). In: ICB, pp. 612–618 (2006)Google Scholar
  10. 10.
    Wang, Y., Yu, S., Wang, Y., Tan, T.: Gait recognition based on fusion of multi-view gait sequences. In: ICB, pp. 605–611 (2006)Google Scholar
  11. 11.
    Walraven, J.A.: Introduction to applications and industries for microelectromechanical systems (MEMS). In: International Test Conference, pp. 674–680 (2003)Google Scholar
  12. 12.
    Ratha, N.K., Connell, J.H., Bolle, R.M.: An analysis of minutiae matching strength. In: Third International Conference on Audio- and Video-Based Biometric Person Authentication, June 2001, pp. 223–228 (2001)Google Scholar
  13. 13.
    Martin, A., Doddington, G., Kamm, T., Ordowski, M., Przybocki, M.: The DET curve in assessment of detection task performance. In: Eurospeech 1997, pp. 1895–1898 (1997)Google Scholar
  14. 14.
    Bolle, R.M., Pankanti, S., Ratha, N.K.: Evaluation techniques for biometrics-based authentication systems (FRR). In: 15th International Conference on Pattern Recognition, September 2000, pp. 831–837 (2000)Google Scholar
  15. 15.
    Ailisto, H.J., Lindholm, M., Mantyjarvi, J., Vildjiounaite, E., Makela, S.-M.: Identifying people from gait pattern with accelerometers. In: Proceedings of SPIE. Biometric Technology for Human Identification II, vol. 5779, pp. 7–14 (March 2005)Google Scholar
  16. 16.
    Mantyjarvi, J., Lindholm, M., Vildjiounaite, E., Makela, S.-M., Ailisto, H.J.: Identifying users of portable devices from gait pattern with accelerometers. In: 2005 IEEE International Conference on Acoustics, Speech, and Signal Processing (March 2005)Google Scholar
  17. 17.
    Bobick, A.F., Johnson, A.Y.: Gait recognition using static, activity-specific parameters. In: Proceedings of the 2001 IEEE Computer Computer Vision and Pattern Recognition, vol. 1, pp. I-423–I-430 (2001)Google Scholar
  18. 18.
    Gafurov, D., Helkala, K., Sondrol, T.: Gait recognition using acceleration from MEMS. In: 1st IEEE International Conference on Availability, Reliability and Security (ARES), Vienna, Austria (April 2006)Google Scholar
  19. 19.
    Lindberg, J., Blomberg, M.: Vulnerability in speaker verification - a study of technical impostor techniques. In: Eurospeech, pp. 1211–1214 (1999)Google Scholar
  20. 20.
    Lau, Y.W., Wagner, M., Tran, D.: Vulnerability of speaker verification to voice mimicking. In: International Symposium on Intelligent Multimedia, Video and Speech Processing, pp. 145–148 (October 2004)Google Scholar
  21. 21.
    Guo, J.K., Doermann, D., Rosenfield, A.: Off-line skilled forgery detection using stroke and sub-stroke properties. In: 15th International Conference on Pattern Recognition, September 2000, pp. 355–358 (2000)Google Scholar
  22. 22.
    Cha, S.-H., Tappert, C.C.: Automatic detection of handwriting forgery. In: Eighth International Workshop on Frontiers in Handwriting Recognition, pp. 264–267 (August 2002)Google Scholar
  23. 23.
    Vaughan, C., Davis, B., O’Cononor, J.: Dynamics of human gait. Kiboho Publishers (1999)Google Scholar
  24. 24.
    Huang, P.: Promoting Wearable Computing: A Survey and Future Agenda. In: Proc. of International Conference on Information Society in The 21st Century: Emerging Technologies and New Challenges (November 2000)Google Scholar
  25. 25.
    Starner, T.E.: Attention, memory, and wearable interfaces. IEEE Pervasive Computing 1(4), 88–91 (2002)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Davrondzhon Gafurov
    • 1
  • Einar Snekkenes
    • 1
  • Tor Erik Buvarp
    • 1
  1. 1.Norwegian Information Security Lab, Department of Computer Science and Media TechnologyGjovik University CollegeGjovikNorway

Personalised recommendations