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Machine Learning Approach for Gesture Recognition Based on Automatic Feature Selection
Xiubo Liang, Franck Multon, Weidong Geng
Motion in Games, November 2012, pp. 366--369.

Abstract: In this paper we propose a machine learning approach to design strong classifiers based on the most relevant combination of 1444 weak classifiers based on pose parameters. This classifier is embedded in a three-layers recognition system which enables us to recognize 70 different gestures performed by various users with high style variability; the recognition ratio is 97.5% with our approach.

Article URL: http://dx.doi.org/10.1007/978-3-642-34710-8_34

BibTeX format:

@incollection{Liang:2012:MLA,
  author = {Xiubo Liang and Franck Multon and Weidong Geng},
  title = {Machine Learning Approach for Gesture Recognition Based on Automatic Feature Selection},
  booktitle = {Motion in Games},
  pages = {366--369},
  month = nov,
  year = {2012},
}

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