Hierarchical Elastic Graph Matching for Hand Gesture Recognition

  • Yu-Ting Li
  • Juan P. Wachs
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7441)


This paper proposes a hierarchical scheme for elastic graph matching hand posture recognition. The hierarchy is expressed in terms of weights assigned to visual features scattered over an elastic graph. The weights in graph’s nodes are adapted according to their relative ability to enhance the recognition, and determined using adaptive boosting. A dictionary representing the variability of each gesture class is proposed, in the form of a collection of graphs (a bunch graph). Positions of nodes in the bunch graph are created using three techniques: manually, semi-automatic, and automatically. The recognition results show that the hierarchical weighting on features has significant discriminative power compared to the classic method (uniform weighting). Experimental results also show that the semi-automatically annotation method provides efficient and accurate performance in terms of two performance measures; cost function and accuracy.


Elastic bunch graph Graph matching Feature hierarchy Hand gesture recognition 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Yu-Ting Li
    • 1
  • Juan P. Wachs
    • 1
  1. 1.Department of Industrial EngineeringPurdue UniversityWest LafayetteU.S.A

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