Dynamic gesture recognition based on fuzzy neural network classifier

Ching Han Chen, Nai Yuan Liu, Kirk Chang, Gimmy Su

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

This paper presents a dynamic gesture recognition method based on the combination of the fuzzy features of the dynamic gesture track changes and the fuzzy neural network inference system. This method first classified the dynamic gestures roughly into circular gestures and linear gestures. Further, gestures were classified narrowly into up, down, left, right, clockwise, and counter-clockwise gestures. These six dynamic gestures, which are commonly used in IP-TV controlling, were introduced as the recognition goal in our dynamic gesture recognition system. The results show that this method has a good recognition performance and fault tolerance, and more applicable to real gesture-controlled human-computer interactive environment.

Original languageEnglish
Title of host publicationACHI 2013 - 6th International Conference on Advances in Computer-Human Interactions
EditorsLeslie Miller
PublisherInternational Academy, Research and Industry Association, IARIA
Pages57-61
Number of pages5
ISBN (Electronic)9781612082509
StatePublished - 2013
Event6th International Conference on Advances in Computer-Human Interactions, ACHI 2013 - Nice, France
Duration: 24 Feb 20131 Mar 2013

Publication series

NameACHI 2013 - 6th International Conference on Advances in Computer-Human Interactions

Conference

Conference6th International Conference on Advances in Computer-Human Interactions, ACHI 2013
Country/TerritoryFrance
CityNice
Period24/02/131/03/13

Keywords

  • Fuzzy system
  • Gesture recognition
  • Neural network

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