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Gesture Recognition Under Small Sample Size (2007)
| Citations: | 8 - 2 self |
BibTeX
@MISC{Kim07gesturerecognition,
author = {Tae-kyun Kim and Roberto Cipolla},
title = {Gesture Recognition Under Small Sample Size},
year = {2007}
}
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Abstract
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.
Keyphrases
small sample size gesture recognition pairwise feature extraction method discriminant function robust gesture recognition video volume small amount hand gesture class input space canonical correlation analysis high dimensionality traditional classifier discriminative spatiotemporal feature direct use training sample
