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Hand Gestures Categorisation and Recognition

  • Maleika Heenaye-Mamode KhanEmail author
  • Nishtabye Ittoo
  • Bonie Kathiana Coder
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 863)

Abstract

In this digital era, the focus is now on the development of applications that allow human beings and machines to interact directly. Up to now, many hand gesture recognition systems have been developed for different applications such as sign language recognition and smart surveillance. In recent years, researchers have shown interest in the development of hand gesture recognition applications for dancing movements, which involve dynamic hand gestures. However, there are still various challenges such as extraction of invariant factors, automatic segmentation, the transition between gestures, mixed gestures issues, nature of dynamic hand gestures and occlusions that need to be addressed. This research work aims at developing an application to categorise and recognise the classical “Bharatanatyam” dance hand gestures. Since no online database of “Bharatanatyam” gestures is available to the public for research purposes, a customised database has been built with 900 images, consisting of 15 instances for each hand gesture. In this work, Chain Codes and Histogram of Oriented Gradients (HOG) are proposed for the feature representation of the hand gestures. For the classification of the images, Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) are explored. From the experiments conducted, Chain codes with SVM provide a recognition rate of 99.9% and a false rejection rate of only 0.1%, which is a promising technique for the deployment of movement recognition applications.

Keywords

Hand gestures Chain codes HOG SVM KNN 

Notes

Declaration

We have taken the required permission of dataset, images used in this work from the respective authorities. We are solely responsible if any problem that arises in the future.

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Maleika Heenaye-Mamode Khan
    • 1
    Email author
  • Nishtabye Ittoo
    • 1
  • Bonie Kathiana Coder
    • 1
  1. 1.University of MauritiusMokaMauritius

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