Advertisement

Gesture Signature for Ambient Intelligence Applications: A Feasibility Study

  • Elisabetta Farella
  • Sile O’Modhrain
  • Luca Benini
  • Bruno Riccó
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3968)

Abstract

This work investigates the feasibility of a personal verification system using gestures as biometric signatures. Gestures are captured by low-power, low-cost tri-axial accelerometers integrated into an expansion pack for palmtop computers. The objective of our study is to understand whether the mobile system can recognize its owner by how she/he performs a particular gesture, acting as a gesture signature. The signature can be used for obtaining access to the mobile device, but the handheld device can also act as an intelligent key to provide access to services in an ambient intelligence scenario. Sample gestures are analyzed and classified using supervised and unsupervised dimensionality reduction techniques. Results on a set of benchmark gestures performed by several individuals are encouraging.

Keywords

Mobile Device Dimensionality Reduction Gesture Recognition Hand Gesture Inertial Sensor 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Oakley, I., Angesleva, J., Hughes, S., O’Modhrain, S.: Tilt and Fill:Scrolling with Vibrotactile Display. In: Proc. of EuroHaptics (2003)Google Scholar
  2. 2.
  3. 3.
    Pirhonen, A., Brewster, S.A., Holguin, C.: Gestural and Audio Metaphors as a Means of Control for Mobile Devices. In: ACM CHI 2002, Minneapolis (2002)Google Scholar
  4. 4.
    Hinckley, K., Pierce, J., Sinclair, M., Horvitz, E.: Sensing Techniques for Mobile Interaction. In: ACM WIST (2000)Google Scholar
  5. 5.
    Harrison, B., et al.: Squeeze Me, Hold Me, Tilt Me! An Exploration of Manipulative User Interfaces. In: CHI 1998 (1998)Google Scholar
  6. 6.
    Strachan, S., Murray-Smith, R., Oakley, I., Angesleva, J.: Dynamic Primitives for Gestural Interaction. In: Mobile HCI (2004)Google Scholar
  7. 7.
    Kobayashi, T., Sugiyama, K.: Hand Image Recognition for Code Numbers. In: ICITA 2002 (2002)Google Scholar
  8. 8.
    Patel, S.N., Pierce, J., Abowd, G.D.: A Gesture-based Authentication Scheme for Untrusted Public Terminals. In: Proc. of UIST 2004 (2004)Google Scholar
  9. 9.
  10. 10.
    Gupta, D.: Computer Gesture Recognition: Using the Constellation Method. Caltech Undergraduate Research Journal (2001)Google Scholar
  11. 11.
    Collins, R., Gross, R., Shi, J.O.: Silhouette-based Human Identification from Body Shape and Gait. In: 5th Intl Conference on Automatic Face and Gesture Recognition (2002)Google Scholar
  12. 12.
    Murray, M.P.: Gait as a total pattern of movement. American journal of Physical medicine 46, 290–333 (1967)Google Scholar
  13. 13.
    Johansson, G.: Visual perception of biological motion and a model for its analysism. Perception and Psychophysics (1973)Google Scholar
  14. 14.
    Perng, J.K., Fisher, B., Hollar, S., Pister, K.S.J.: Acceleration Sensing Glove (ASG). In: IEEE Symposium on Wearable Computers, pp. 178–180 (1999)Google Scholar
  15. 15.
    Strachan, S., Murray-Smith, R.: Muscle Tremor as an Input Mechanism. In: Proc. of UIST 2004, Santa Fe (2004)Google Scholar
  16. 16.
  17. 17.
    Tenenbaum, J.B., de Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290, 2319–2323 (2000)CrossRefGoogle Scholar
  18. 18.
    Jolliffe, I.T.: Principal Component Analysis. Springer, New York (1986)CrossRefMATHGoogle Scholar
  19. 19.
  20. 20.
    Devroye, L., GyorfiA, L.: Probabilistic Theory of Pattern Recognition. Springer, Heidelberg (1996)CrossRefGoogle Scholar
  21. 21.
    Roweis, S.T., Saul, L.K.: Nonlinear Dimensionality Reduction by Locally Linear Embedding. Science 290, 2323–2326 (2000)CrossRefGoogle Scholar
  22. 22.
    Saul, L., Roweis, S.: Think Globally: Fit Locally: Unsupervised Learning of Nonlinear Manifolds. Tech. Report MS CIS-02-18. University of Pennsylvania (2002)Google Scholar
  23. 23.
    Saul, L.K., Roweis, S.T.: Think Globally: Fit Locally: Unsupervised Learning of Low Dimensional Manifolds. J. Mach. Learn. Res. 4, 119–155 (2003)MathSciNetMATHGoogle Scholar
  24. 24.
    Hughes, S., Oakley, I., O’Modhrain, S.: MESH: Supporting Mobile Multi-modal Interfaces. In: Proc. of ACM UIST 2004 (2004)Google Scholar
  25. 25.
    Boulétreau, V., Vincent, N., Sabourin, R., Emptoz, H.: Handwriting and Signature: One or Two Personality Identifiers? In: International Conference on Pattern Recognition (ICPR 1998), pp. 63–84 (1998)Google Scholar
  26. 26.
    Bang, W., Chang, W., Kang, K., Potanin, E.C., Kim, A.D.: Self-contained spatial input device for wearable computers. In: Proc. of Seventh IEEE International Symposium on Wearable Computers, pp. 26–34 (2003)Google Scholar
  27. 27.
    Pankanti, S., Bolle, R., Jain, A.K.: Special issue of IEEE Computer on Biometrics. 33, I. 2 (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Elisabetta Farella
    • 1
  • Sile O’Modhrain
    • 2
  • Luca Benini
    • 1
  • Bruno Riccó
    • 1
  1. 1.DEIS University of BolognaBolognaItaly
  2. 2.Sonic Arts Research CentreQueens UniversityBelfastIreland

Personalised recommendations