Finger Tracking Methods Using EyesWeb

  • Anne-Marie Burns
  • Barbara Mazzarino
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3881)

Abstract

This paper compares different algorithms for tracking the position of fingers in a two-dimensional environment. Four algorithms have been implemented in EyesWeb, developed by DIST-InfoMus laboratory. The three first algorithms use projection signatures, the circular Hough transform, and geometric properties, and rely only on hand characteristics to locate the finger. The fourth algorithm uses color markers and is employed as a reference system for the other three. All the algorithms have been evaluated using two-dimensional video images of a hand performing different finger movements on a flat surface. Results about the accuracy, precision, latency and computer resource usage of the different algorithms are provided. Applications of this research include human-computer interaction systems based on hand gesture, sign language recognition, hand posture recognition, and gestural control of music.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Camurri, M., Mazzarino, B., Volpe, G.: Analysis of Expressive Gesture: The EyesWeb Expressive Gesture processing Library. In: Camurri, A., Volpe, G. (eds.) GW 2003. LNCS (LNAI), vol. 2915, pp. 460–467. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  2. 2.
    Chan, S.C.: Hand Gesture Recognition. Center for Intelligent Machines, McGill University (2004), http://www.cim.mcgill.ca/~schan19/research/research.html
  3. 3.
    Duda, S.R.D., Hart, P.E.: Use of the Hough Transform to Detect Lines and Curves in Pictures in Communications of the Association of Computing Machinery 15, 11–15 (1972)CrossRefGoogle Scholar
  4. 4.
    Hemmi, K.: On the Detecting Method of Fingertip Positions Using the Circular Hough Transform. In: Proceeding of the 5th Asia-Pacific Conference on Control and Measurement (2002)Google Scholar
  5. 5.
    Illingworth, J., Kittler, J.: A Survey of the Hough Transform in Computer Vision, Graphics, and Image Processing 44, 87–116 (1988)Google Scholar
  6. 6.
    Kohler, M.: Vision Based Hand Gesture Recognition Systems, Computer Graphics, University of Dortmund, http://ls7-www.cs.uni-dortmund.de/research/gesture/vbgr-table.html
  7. 7.
    Koike, H., Sato, Y., Kobayashi, Y.: Integrating Paper and Digital Information on EnhancedDesk: A Method for Realtime Finger Tracking on an Augmented Desk System. ACM Transaction on Computer-Human Interaction 8(4), 307–322 (2001)CrossRefGoogle Scholar
  8. 8.
    Letessier, J., Brard, F.: Visual Tracking of Bare Fingers for Interactive Surfaces. In: Seventeenth Annual ACM Symposium on User Interface Software and Technology, vol. 6(2), pp. 119–122 (2004)Google Scholar
  9. 9.
    Pavlovic, V.I., Sharma, R., Huang, T.S.: Visual Interpretation of Hand Gestures for Human-Computer Interaction: A Review. IEEE Transactions on pattern analysis and machine intelligence 19(7), 677–695 (1997)CrossRefGoogle Scholar
  10. 10.
    Schulze, M.A.: Cicular Hough Transform A Java Applet Demonstration (2003), http://www.markschulze.net/java/hough/
  11. 11.
    Yörük, E., Dutağaci, H., Sankur, B.: Hand Biometrics, Electrical and Electronic Engineering Department, Boğaziçi University (to appear)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Anne-Marie Burns
    • 1
  • Barbara Mazzarino
    • 2
  1. 1.Input Devices and Music Interaction Lab, Schulich School of MusicMcGill UniversityMontréalCanada
  2. 2.InfoMus LabDIST – University of GenovaGenovaItaly

Personalised recommendations