Gesture Recognition Under Small Sample Size

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


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.


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.


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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

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