Advertisement

Gesture Recognition Under Small Sample Size

  • Tae-Kyun Kim
  • Roberto Cipolla
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4843)

Abstract

This paper addresses gesture recognition under small sample size, where direct use of traditional classifiers is difficult due to high dimensionality of input space. We propose a pairwise feature extraction method of video volumes for classification. The method of Canonical Correlation Analysis is combined with the discriminant functions and Scale-Invariant-Feature-Transform (SIFT) for the discriminative spatiotemporal features for robust gesture recognition. The proposed method is practically favorable as it works well with a small amount of training samples, involves few parameters, and is computationally efficient. In the experiments using 900 videos of 9 hand gesture classes, the proposed method notably outperformed the classifiers such as Support Vector Machine/Relevance Vector Machine, achieving 85% accuracy.

Keywords

Support Vector Machine Canonical Correlation Canonical Correlation Analysis Gesture Recognition Relevance Vector Machine 
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.
    Björck, Å., Golub, G.H.: Numerical methods for computing angles between linear subspaces. Mathematics of Computation 27(123), 579–594 (1973)Google Scholar
  2. 2.
    Bowden, R., Windridge, D., Kadir, T., Zisserman, A., Brady, M.: A linguistic feature vector for the visual interpretation of sign language. In: ECCV, pp. 390–401 (2004)Google Scholar
  3. 3.
    Darrell, T., Pentland, A.: Space-time gestures. In: Proc. of CVPR, pp. 335–340 (1993)Google Scholar
  4. 4.
    Efros, A.A., Berg, A.C., Mori, G., Malik, J.: Recognizing action at a distance. In: Proc. of ICCV, pp. 726–733 (2003)Google Scholar
  5. 5.
    Freeman, W., Roth, M.: Orientation histogram for hand gesture recognition. In: Int’l Conf. on Automatic Face and Gesture Recognition (1995)Google Scholar
  6. 6.
    Just, A., Rodriguez, Y., Marcel, S.: Hand posture classification and recognition using the modified census transform. In: Int’l Conf. on Automatic Face and Gesture Recognition (2006)Google Scholar
  7. 7.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision 60(2), 91–110 (2004)CrossRefGoogle Scholar
  8. 8.
    Shechtman, E., Irani, M.: Space-time behavior based correlation. In: Proc. of CVPR 2005, pp. 405–412 (2005)Google Scholar
  9. 9.
    Starner, T., Pentland, A., Weaver, J.: Real-time american sign language recognition using desk and wearable computer based video. IEEE Trans. Pattern Anal. Mach. Intell. 20(12), 1371–1375 (1998)CrossRefGoogle Scholar
  10. 10.
    Kim, T., Kittler, J., Cipolla, R.: Discriminative learning and recognition of image set classes using canonical correlations. IEEE Trans. on PAMI 29(6), 1005–1018 (2007)Google Scholar
  11. 11.
    Wong, S., Cipolla, R.: Real-time interpretation of hand motions using a sparse bayesian classifier on motion gradient orientation images. In: Proc. of BMVC 2005, pp. 379–388 (2005)Google Scholar
  12. 12.
    Niebles, J.C., Wang, H., Fei-Fei, L.: Unsupervised learning of human action categories using spatial-temporal words. In: BMVC (2006)Google Scholar
  13. 13.
    Schuldt, C., Laptev, I., Caputo, B.: Recognizing human actions: A local SVM approach. In: ICPR 2004, pp. 32–36 (2004)Google Scholar
  14. 14.
    Kim, T., Wong, S., Cipolla, R.: Tensor Canonical Correlation Analysis for Action Classification. In: CVPR (2007)Google Scholar
  15. 15.
    Hardoon, D., Szedmak, S., Taylor, J.S.: Canonical correlation analysis; An overview with application to learning methods. Neural Computation 16(12), 639–2664 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Tae-Kyun Kim
    • 1
  • Roberto Cipolla
    • 2
  1. 1.Sidney Sussex College, University of Cambridge, Cambridge, CB2 3HUUK
  2. 2.Department of Engineering, University of Cambridge, Cambridge, CB2 1PZUK

Personalised recommendations