Real-Time Adaptive Hand Motion Recognition Using a Sparse Bayesian Classifier

  • Shu-Fai Wong
  • Roberto Cipolla
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3766)


An approach to increase adaptability of a recognition system, which can recognise 10 elementary gestures and be extended to sign language recognition, is proposed. In this work, recognition is done by firstly extracting a motion gradient orientation image from a raw video input and then classifying a feature vector generated from this image to one of the 10 gestures by a sparse Bayesian classifier. The classifier is designed in a way that it supports online incremental learning and it can be thus re-trained to increase its adaptability to an input captured under a new condition. Experiments show that the accuracy of the classifier can be boosted from less than 40% to over 80% by re-training it using 5 newly captured samples from each gesture class. Apart from having a better adaptability, the system can work reliably in real-time and give a probabilistic output that is useful in complex motion analysis.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Starner, T., Weaver, J., Pentland, A.: Real-time american sign language recognition using desk and wearable computer based video. PAMI 20, 1371–1375 (1998)Google Scholar
  2. 2.
    Vogler, C., Metaxas, D.: A framework for recognizing the simultaneous aspects of american sign language. CVIU 81, 358–384 (2001)zbMATHGoogle Scholar
  3. 3.
    Bauer, B., Kraiss, K.F.: Video-based sign recognition using self-organizing subunits. In: Proc. ICPR, pp. 282–296 (2002)Google Scholar
  4. 4.
    Wilson, A., Bobick, A.: Realtime online adaptive gesture recognition. In: Proc. ICPR, vol. I, pp. 270–275 (2000)Google Scholar
  5. 5.
    Bowden, R., Windridge, D., Kadir, T., Zisserman, A., Brady, M.: A linguistic feature vector for the visual interpretation of sign language. In: Proc. ECCV, vol. I, pp. 390–401 (2004)Google Scholar
  6. 6.
    Derpanis, K.G., Wildes, R.P., Tsotsos, J.K.: Hand gesture recognition within a linguistics-based framework. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3021, pp. 282–296. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  7. 7.
    Stokoe, W.C., Casterline, D., Croneberg, C.: A Dictionary of American Sign Language. Linstok Press, Washington (1965)Google Scholar
  8. 8.
    Tipping, M.E.: Sparse bayesian learning and the relevance vector machine. The Journal of Machine Learning Research 1, 211–244 (2001)zbMATHCrossRefMathSciNetGoogle Scholar
  9. 9.
    Tipping, M.E., Faul, A.C.: Fast marginal likelihood maximization for sparse bayesian models. In: Proceedings of the Ninth International Workshop on Artificial Intelligence and Statistics (2003)Google Scholar
  10. 10.
    Bradski, G.R., Davis, J.W.: Motion segmentation and pose recognition with motion history gradients. Machine Vision and Applications 13, 174–184 (2002)CrossRefGoogle Scholar
  11. 11.
    Bobick, A.F., Davis, J.W.: The recognition of human movement using temporal templates. PAMI 23, 257–267 (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Shu-Fai Wong
    • 1
  • Roberto Cipolla
    • 1
  1. 1.Department of EngineeringThe University of Cambridge 

Personalised recommendations