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Hand Gesture Recognition using Improved Hidden Markov Models

  • Xu, Wenkai (Dept. of Information Communication Engineering, Tongmyong University) ;
  • Lee, Eung-Joo (Dept. of Information Communication Engineering, Tongmyong University)
  • Received : 2011.03.23
  • Accepted : 2011.06.01
  • Published : 2011.07.30

Abstract

In this paper, an improved method of hand detecting and hand gesture recognition is proposed, it can be applied in different illumination condition and complex background. We use Adaptive Skin Threshold (AST) to detect the areas of hand. Then the result of hand detection is used to hand recognition through the improved HMM algorithm. At last, we design a simple program using the result of hand recognition for recognizing "stone, scissors, cloth" these three kinds of hand gesture. Experimental results had proved that the hand and gesture can be detected and recognized with high average recognition rate (92.41%) and better than some other methods such as syntactical analysis, neural based approach by using our approach.

References

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Cited by

  1. Human-Computer Natur al User Inter face Based on Hand Motion Detection and Tracking vol.15, pp.4, 2012, https://doi.org/10.9717/kmms.2012.15.4.501
  2. Human Gesture Recognition Technology Based on User Experience for Multimedia Contents Control vol.15, pp.10, 2012, https://doi.org/10.9717/kmms.2012.15.10.1196
  3. Dynamic Human Activity Recognition Based on Improved FNN Model vol.15, pp.4, 2012, https://doi.org/10.9717/kmms.2012.15.4.417