Static Hand Gesture Recognition Based on Fusion of Moments

  • Subhamoy Chatterjee
  • Dipak Kumar Ghosh
  • Samit Ari
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 309)

Abstract

A vision-based static hand gesture recognition algorithm which consists of three stages: pre-processing, feature extraction and classification are presented in this work. The pre-processing stage comprises of following three sub-stages: segmentation, which segments hand region from its background using YCbCr skin colour-based segmentation process; rotation, that rotates segmented gesture to make the algorithm, rotation invariant; Morphological filtering, that removes background and object noise. Non-orthogonal moments like geometric moments and orthogonal moments like Tchebichef and Krawtchouk moments are used here as features. To improve the performance of classification, two feature fusion strategies are proposed in this work: serial feature fusion and parallel feature fusion. A feed-forward multi-layer perceptron (MLP)-based artificial neural network classifier is proposed. A user-independent experiment is conducted on 1,500 gestures of 10 classes for 10 different users.

Keywords

American sign language digits Geometric moment Tchebichef moment Krawtchouk moment Serial feature fusion Parallel feature fusion Artificial neural network 

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Copyright information

© Springer India 2015

Authors and Affiliations

  • Subhamoy Chatterjee
    • 1
  • Dipak Kumar Ghosh
    • 1
  • Samit Ari
    • 1
  1. 1.Department of Electronics and Communication EngineeringNational Institute of TechnologyRourkelaIndia

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