Robust modeling and recognition of hand gestures with dynamic Bayesian network

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

6 Citations (Scopus)

Abstract

In this paper, we propose a new gesture recognition model for a set of both one-hand and two-hand gestures based on the dynamic Bayesian network framework which makes it easy to represent the relationship among features and incorporate new information to the model. Unlike the coupled HMM, the proposed model has room for common hidden variables which are believed to be shared between two variables. In an experiment with ten isolated gestures, we obtained a recognition rate upwards of 99.59% with leave-one-out 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 19th International Conference on Pattern Recognition, ICPR 2008
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Print)9781424421756
DOIs
Publication statusPublished - 2008

Publication series

NameProceedings - International Conference on Pattern Recognition
ISSN (Print)1051-4651

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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    Suk, H. I., Sin, B. K., & Lee, S. W. (2008). Robust modeling and recognition of hand gestures with dynamic Bayesian network. In 2008 19th International Conference on Pattern Recognition, ICPR 2008 [4761337] (Proceedings - International Conference on Pattern Recognition). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/icpr.2008.4761337