Cluster labeling and parameter estimation for the automated setup of a hand-gesture recognition system

Juan P. Wachs, Helman Stern, Yael Edan

Research output: Contribution to journalArticlepeer-review

42 Scopus citations


In this work, we address the issue of reconfigurability of a hand-gesture recognition system. The calibration or setup of the operational parameters of such a system is a time-consuming effort, usually performed by trial and error, and often causing system performance to suffer because of designer impatience. In this work, we suggest a methodology using a neighborhood-search algorithm for tuning system parameters. Thus, the design of hand-gesture recognition systems is transformed into an optimization problem. To test the methodology, we address the difficult problem of simultaneous calibration of the parameters of the image processing/fuzzy C-means (FCM) components of a hand-gesture recognition system. In addition, we proffer a method for supervising the FCM algorithm using linear programming and heuristic labeling. Resulting solutions exhibited fast convergence (in the order of ten iterations) to reach recognition accuracies within several percent of the optimal. Comparative performance testing using three gesture databases (BGU, American Sign Language and Gripsee), and a real-time implementation (Tele-Gest) are reported on.

Original languageEnglish
Pages (from-to)932-944
Number of pages13
JournalIEEE Transactions on Systems, Man, and Cybernetics Part A:Systems and Humans
Issue number6
StatePublished - 1 Nov 2005


  • Automated setup
  • Cluster labeling
  • Fuzzy c-means
  • Gesture recognition
  • Hand gestures
  • Neighborhood search
  • Supervised clustering
  • Telerobotics

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Human-Computer Interaction
  • Computer Science Applications
  • Electrical and Electronic Engineering


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