Neural Computing and Applications

, Volume 25, Issue 3–4, pp 733–741 | Cite as

RETRACTED ARTICLE: Feature extraction and ML techniques for static gesture recognition

  • Haitham Badi
  • Sabah Hasan Hussein
  • Sameem Abdul Kareem
Original Article

Abstract

The main objective of this study is to explore the utility of a neural network-based approach in hand gesture recognition. The proposed system presents two recognition algorithms to recognize a set of six specific static hand gestures, namely open, close, cut, paste, maximize, and minimize. The hand gesture image is passed through three stages: preprocessing, feature extraction, and classification. In the first method, the hand contour is used as a feature that treats scaling and translation of problems (in some cases). However, the complex moment algorithm is used to describe the hand gesture and to treat the rotation problem in addition to scaling and translation. The back-propagation learning algorithm is employed in the multilayer neural network classifier. The second method proposed in this article achieves better recognition rate than the first method.

Keywords

Gesture recognition Human–computer interaction Representations Recognition Natural interfaces 

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Copyright information

© Springer-Verlag London 2014

Authors and Affiliations

  • Haitham Badi
    • 1
  • Sabah Hasan Hussein
    • 2
  • Sameem Abdul Kareem
    • 1
  1. 1.Faculty of Computer Science and Information of TechnologyUniversity of MalayaKuala LumpurMalaysia
  2. 2.Al Yarmouk University CollegeBaghdadIraq

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