Using Radial Basis Function Networks for Hand Gesture Recognition

  • R. Salomon
  • J. Weissmann
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 67)


This chapter is about a data-glove/neural network system as a powerful input device for virtual reality and multi media applications. In contrast to conventional keyboards, space balls, and two-dimensional mice, which allow for only rudimental inputs, the data-glove system allows the user to present the system with a rich set of intuitive commands. Previous research has employed different neural networks to recognize various hand gestures. Due to their on-line adaptation capabilities, radial basis function networks are preferable to backpropagation. Unfortunately, the latter have shown better recognition rates. This chapter describes the application and discusses the performance of various radial basis function networks for hand gesture recognition. This chapter furthermore applies evolutionary algorithms to fine tune pre-learned radial basis function networks. After optimization, the network achieves a recognition rate of up to 100%, and is therefore comparable or even better than that of back-propagation networks.


Radial Basis Function Evolution Strategy Recognition Rate Gesture Recognition Radial Basis Function Network 
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]
    Ascension Technology Corporation, P.O. Box 527, Burlington, VT 05402,, http://www.Ascension-tech.corn.
  2. [2]
    Bäck, T., Hammel, U., and Schwefel, H.-P. (1997), “Evolutionary computation: comments on the history and current state,” IEEE Transactions on Evolutionary Computation, vol. 1, no. 1, pp. 3–17.CrossRefGoogle Scholar
  3. [3]
    Bäck, T. and Schwefel, H.–P. (1993), “An overview of evolutionary algorithms for parameter optimization, ” Evolutionary Computation, vol. 1, no. 1, pp. 1–23.CrossRefGoogle Scholar
  4. [4]
    Fogel, L.J. (1962), “Autonomous automata,” Industrial Research, vol. 4, pp. 14–19.Google Scholar
  5. [5]
    Fogel, D.B. (1995), Evolutionary Computation: Toward a New Philosophy of Machine Learning Intelligence, IEEE Press, Jersy, NJ.Google Scholar
  6. [6]
    Goldberg, D.E. (1989), Genetic Algorithms in Search, Optimization and Machine Learning, Addison–Wesley, Reading, MA.Google Scholar
  7. [7]
    Hertz, J., Krogh, A., and Palmer, R. (1991), Introduction to the Theory of Neural Computation, Addison–Wesley Publishing Company, Redwood City, CA.Google Scholar
  8. [8]
    Kamarthi, S.V. and Pittner, S. (1999), “Accelerating neural network training using weight extrapolations,” Neural Networks, vol. 12, pp. 1285–1299.CrossRefGoogle Scholar
  9. [9]
    Kieldsen, R. and Kender, J. (1996), “Toward the use of gesture in traditional user interfaces,” Proceedings of the Second International Conference on Automatic Face and Gesture Recognition, IEEE, pp. 151–156.Google Scholar
  10. [10]
    Mühlenbein, H. and Schlierkamp-Voosen, D. (1993), “Predictive models for the breeder genetic algorithm I,” Evolutionary Computation, vol. 1, no. 1, pp. 25–50.CrossRefGoogle Scholar
  11. [11]
    Polhemus Inc., 1 Hercules Drive, P.O. Box 560, Colchester, VT 05446, Scholar
  12. [12]
    Press, W.H., Flannery, B.P., Teukolsky, S.A., and Vetterling, W.T. (1987), Numerical Recipes in C: the Art of Scientific Computing,Cambridge University Press.Google Scholar
  13. [13]
    Production and distribution of data gloves and related devices. Virtual Technologies, Inc., 2175 Park Boulevard, Palo Alto, CA 94306,
  14. [14]
    Rechenberg, I. (1973), Evolutionsstrategie,Frommann-Holzboog, Stuttgart. Also printed in [15]. (In German.)Google Scholar
  15. [15]
    Rechenberg, I. (1994), Evolutionsstrategie,Frommann-Holzboog, Stuttgart. (In German.)Google Scholar
  16. [16]
    Rojas, R. (1996), Neural Networks: a Systematic Introduction, Springer–Verlag, Berlin, Germany.Google Scholar
  17. [17]
    Schlierkamp-Voosen, D. and Mühlenbein, H. (1994), “Strategy adaptation by competing subpopulations,” in Davidor, Y., Schwefel, H.–P., and Männer, R. (Eds.), Parallel Problem Solving from Nature (PPSN III), Springer–Verlag Berlin, pp. 199–208.CrossRefGoogle Scholar
  18. [18]
    Schwefel, H.-P. (1995), Evolution and Optimum Seeking, John Wiley and Sons, NY.Google Scholar
  19. [19]
    Weissmann, J. and Salomon, R. (1999), “Gesture recognition for virtual reality applications using data gloves and neural networks,” Proceedings of the 1999 International Joint Conference on Neural Networks, IEEE.Google Scholar
  20. [20]
    d’Ydewalle, G. et al. (1995), “Graphical versus character—based word processors: an analysis of user performance,” Behaviour and Information Technology, vol. 14, no. 4, pp. 208–214.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • R. Salomon
  • J. Weissmann

There are no affiliations available

Personalised recommendations