Gesture Modeling and Recognition Using Finite State Machines

  • Authors:
  • Pengyu Hong;Thomas S. Huang;Matthew Turk

  • Affiliations:
  • -;-;-

  • Venue:
  • FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
  • Year:
  • 2000

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Abstract

We propose a state based approach to gesture learning and recognition. Using Soatial Clustering and Temporal alignment, each gesture is defined to be an ordered sequence of states in spatial-temporal 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.