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Machine Learning Approach for Gesture Recognition Based on Automatic Feature Selection

  • Xiubo Liang
  • Franck Multon
  • Weidong Geng
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7660)

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.

Keywords

gesture recognition sign language machine learning HMM automatic feature selection 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Xiubo Liang
    • 1
  • Franck Multon
    • 2
    • 3
  • Weidong Geng
    • 4
  1. 1.College of Software TechnologyZhejiang UniversityNingboChina
  2. 2.M2S, University Rennes2RennesFrance
  3. 3.MimeTICINRIA Rennes, Campus Universitaire de BeaulieuRennesFrance
  4. 4.State Key Lab of CAD&CGZhejiang UniversityHangzhouChina

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