Recognizing hand gestures using dynamic bayesian network

Research output: Chapter in Book/Report/Conference proceedingConference contribution

23 Citations (Scopus)

Abstract

In this paper, we describe a dynamic Bayesian network or DBN based approach to both two-hand gestures and onehand gestures. Unlike wired glove-based approaches, the success of camera-based methods depends greatly on image processing and feature extraction results. So the proposed method of DBN-based inference is preceded by failsafe steps of motion tracking. Then a new gesture recognition model for a set of both one-hand and two-hand gestures is proposed based on the dynamic Bayesian network framework which makes it easy to represent the relationship among features and incorporate new information to the model. In an experiment with ten isolated gestures, we obtained a recognition rate upwards of 99.59% with cross validation. The proposed model is believed to have a strong potential for successful applications to other related problems such as sign languages.

Original languageEnglish
Title of host publication2008 8th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2008
DOIs
Publication statusPublished - 2008
Event2008 8th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2008 - Amsterdam, Netherlands
Duration: 2008 Sep 172008 Sep 19

Publication series

Name2008 8th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2008

Other

Other2008 8th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2008
CountryNetherlands
CityAmsterdam
Period08/9/1708/9/19

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Human-Computer Interaction
  • Electrical and Electronic Engineering

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  • Cite this

    Suk, H. I., Sin, B. K., & Lee, S. W. (2008). Recognizing hand gestures using dynamic bayesian network. In 2008 8th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2008 [4813342] (2008 8th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2008). https://doi.org/10.1109/AFGR.2008.4813342