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.
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An erratum to this article is available at http://dx.doi.org/10.1007/s00521-017-2868-0.
The Editor-in-Chief and the Publisher retract the above-mentioned article due to self-plagiarism. The article has significant overlap with two other publications by the same co-author:
Haitham Sabah Hasan, Sameem Binti Abdul Kareem, Gesture Feature Extraction for Static Gesture Recognition, Arabian J. for Science and Engineering (2013) 38:12. doi:10.1007/s13369-013-0654-6
Haitham Hasan, S. Abdul-Kareem, Static Hand Gesture Recognition Using Neural Networks, Artificial Intelligence Review (2014) 41:2. doi:10.1007/s10462-011-9303-1
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Badi, H., Hussein, S.H. & Kareem, S.A. RETRACTED ARTICLE: Feature extraction and ML techniques for static gesture recognition. Neural Comput & Applic 25, 733–741 (2014). https://doi.org/10.1007/s00521-013-1540-6
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DOI: https://doi.org/10.1007/s00521-013-1540-6