Rapid speedup segment analysis based feature extraction for hand gesture recognition

  • D. Priyanka ParvathyEmail author
  • Kamalraj Subramaniam


The dependency on computers and machines have progressively increased in the past few years and hence human interaction with computers is one of the most actively researched area in science. Many Hand Gesture Recognition (HGR) systems have been developed and they continue to evolve, where in which the gesture interaction becomes smoother and smarter. Gesture recognition is usually implemented in three phases- gesture segmentation, feature extraction and gesture classification, and it’s important that the processes involved in these stages are chosen appropriately such that the misrecognition rate will be kept to a minimum. This paper has proposed a novel algorithm that combines 2D-Discrete Wavelet Transform along with Speed Up Robust Feature (SURF) extraction technique to achieve a robust HGR system that is rotation and scale invariant. The proposed method has achieved an overall classification accuracy of 96.9% with the Radial Basis Function Neural Network (RBFNN) classifier.


Human Computer Interaction multi resolution coiflets wavelet transform Radial basis neural network Cambridge hand gesture 



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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of CSEKarpagam Academy of Higher EducationCoimbatoreIndia
  2. 2.Department of ECEKarpagam Academy of Higher EducationCoimbatoreIndia

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