Hand gesture recognition based on dynamic Bayesian network framework

Research output: Contribution to journalArticle

144 Citations (Scopus)

Abstract

In this paper, we propose a new method for recognizing hand gestures in a continuous video stream using a dynamic Bayesian network or DBN model. The proposed method of DBN-based inference is preceded by steps of skin extraction and modelling, and motion tracking. Then we develop a gesture model for one-or two-hand gestures. They are used to define a cyclic gesture network for modeling continuous gesture stream. We have also developed a DP-based real-time decoding algorithm for continuous gesture recognition. In our experiments with 10 isolated gestures, we obtained a recognition rate upwards of 99.59% with cross validation. In the case of recognizing continuous stream of gestures, it recorded 84% with the precision of 80.77% for the spotted gestures. The proposed DBN-based hand gesture model and the design of a gesture network model are believed to have a strong potential for successful applications to other related problems such as sign language recognition although it is a bit more complicated requiring analysis of hand shapes.

Original languageEnglish
Pages (from-to)3059-3072
Number of pages14
JournalPattern Recognition
Volume43
Issue number9
DOIs
Publication statusPublished - 2010 Sep

Keywords

  • Continuous gesture spotting
  • Coupled hidden Markov model
  • Dynamic Bayesian network
  • Hand gestures recognition

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

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