Gesture modeling and recognition using finite state machines

Pengyu Hong, Matthew Turk, Thomas S. Huang

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

Abstract

We propose a state-based approach to gesture learning and recognition. Using spatial clustering and temporal alignment, each gesture is defined to be an ordered sequence of states in spatialoral space. The 2D image positions of the centers of the head and both hands of the user are used as features; these are located by a color-based tracking method. From training data of a given gesture, we first learn the spatial information and then group the data into segments that are automatically aligned temporally. The temporal information is further integrated to build a finite state machine (FSM) recognizer. Each gesture has a FSM corresponding to it. The computational efficiency of the FSM recognizers allows us to achieve real-time on-line performance. We apply this technique to build an experimental system that plays a game of "Simon Says" with the user.

Original languageEnglish (US)
Title of host publicationProceedings - 4th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2000
PublisherIEEE Computer Society
Pages410-415
Number of pages6
ISBN (Print)0769505805, 9780769505800
DOIs
StatePublished - 2000
Event4th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2000 - Grenoble, France
Duration: Mar 28 2000Mar 30 2000

Publication series

NameProceedings - 4th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2000

Conference

Conference4th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2000
Country/TerritoryFrance
CityGrenoble
Period3/28/003/30/00

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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