Gesture recognition under small sample size

Authors:
Tae-Kyun Kim;Roberto Cipolla
Affiliations:
Sidney Sussex College, University of Cambridge, Cambridge, UK;Department of Engineering, University of Cambridge, Cambridge, UK
Venue:
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part I
Year:
2007

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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.