Using Haptic-Based Trajectory Following in 3D Space to Distinguish between Men and Women

  • Eleni Zarogianni
  • Ioannis Marras
  • Nikos Nikolaidis
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6192)


Gender differences in spatial abilities are widely acknowledged and scientifically proved. In this paper, we explore the feasibility of implementing a behavioral biometrics system capable of distinguishing between men and women, based on a 3D trajectory following test that examines abilities in a spatial context. Haptics were used in order to capture and record various behavioral biometric characteristics such as exerted force, distance from the target trajectory etc. A 83.11% accuracy was observed, suggesting that this novel use of haptics is suitable for this purpose.


haptics behavioral biometrics spatial abilities gender recognition support vector machines 


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  1. 1.
    Voyer, D., Voyer, S., Bryden, M.: Magnitude of sex differences in spatial abilities: a meta-analysis and consideration of critical variables. Psychological Bulletin 117 (1995)Google Scholar
  2. 2.
    Scali, R.M.: Gender differences in spatial task performance as a function of speed or accuracy orientation. Sex Roles 43(5-6), 359–376 (2000)CrossRefGoogle Scholar
  3. 3.
    Osin, P.P., Lee, S., Lee, J.: Gender differences in spatial navigation. In: Proceedings of World Academy of Science, Engineering and Technology, vol. 31 (2007)Google Scholar
  4. 4.
    Moffat, S.D., Hampson, E., Hatzipantelis, M.: Navigation in a virtual maze: Sex differences and correlation with psychometric measures of spatial ability in humans. Evolution and Human Behavior (1998)Google Scholar
  5. 5.
    Zuidhoek, S., Kappers, A.M.L., Postma, A.: Haptic orientation perception: Sex differences and lateralization of functions. Neuropsychologia (2007)Google Scholar
  6. 6.
    Galea, L.A., Kimura, D.: Sex differences in route learning. In: Personality and individual differences (1993)Google Scholar
  7. 7.
    Linn, M.C., Petersen, A.C.: Emergence and characterization of sex differences in spatial ability: A meta-Analysis. Child Development (1985)Google Scholar
  8. 8.
    Orozco, M., Asfaw, A., Adler, A., Shirmohammadi, S., El Saddik, A.: Automatic Identification of participants in haptic systems. In: IEEE Instrument and Measurement Technology Conference, vol. 12, pp. 888–892 (2005)Google Scholar
  9. 9.
    Orozco, M., Graydon, M., Shirmohammadi, S., El Saddik, A.: Using haptic interfaces for user verification in virtual environments. In: IEEE International Conference on Virtual Environments, Human-Computer Interfaces and Measurement Systems, pp. 25–30 (2006)Google Scholar
  10. 10.
    El Saddik, A., Orozco, M., Asfaw, Y., Shirmohammadi, S., Adler, A.: A novel biometric system for identification and verification of haptic users. IEEE Transactions on Instrumentation and Measurement 56(3), 895–906 (2007)CrossRefGoogle Scholar
  11. 11.
    Kanneh, A., Sakr, Z.: Biometric user verification using haptics and fuzzy logic. In: Proceedings of the 16th ACM International Conference on Multimedia, pp. 937–940 (2008)Google Scholar
  12. 12.
    SensAble Technologies,
  13. 13.
  14. 14.
    Vapnik, V.: The Nature of statistical learning theory. Springer, New York (1995)CrossRefzbMATHGoogle Scholar
  15. 15.
    Geisser, S.: Predictive inference: An introduction. Chapman and Hall, New York (1993)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Eleni Zarogianni
    • 1
  • Ioannis Marras
    • 1
  • Nikos Nikolaidis
    • 1
  1. 1.Department of InformaticsAristotle University of ThessalonikiGreece

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