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Philosophy of gustatory perception strategy for hand gesture recognition

Philosophy of gustatory perception strategy for hand gesture recognition Purpose – Nature is the single and most complex system that has been always studied, and no one can compete Mother Nature, but we can learn from her, by many new methodologies through biology. The paper aims to discuss this issue. Design/methodology/approach – In this paper, being inspired by the mechanism through which our Mother Nature handling human taste, a proposed model for clustering and classification hand gesture is introduced based on human taste controlling strategy. Findings – The model can extract information from measurement data and handling it as the structure of tongue and the nervous systems of human taste recognition. Originality/value – The efficiency of proposed model is demonstrated experimentally on classifying the sign language data set; in the high recognition accuracy obtained for numbers of ASL was 95.52 percent. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Kybernetes Emerald Publishing

Philosophy of gustatory perception strategy for hand gesture recognition

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Publisher
Emerald Publishing
Copyright
Copyright © Emerald Group Publishing Limited
ISSN
0368-492X
DOI
10.1108/K-03-2014-0047
Publisher site
See Article on Publisher Site

Abstract

Purpose – Nature is the single and most complex system that has been always studied, and no one can compete Mother Nature, but we can learn from her, by many new methodologies through biology. The paper aims to discuss this issue. Design/methodology/approach – In this paper, being inspired by the mechanism through which our Mother Nature handling human taste, a proposed model for clustering and classification hand gesture is introduced based on human taste controlling strategy. Findings – The model can extract information from measurement data and handling it as the structure of tongue and the nervous systems of human taste recognition. Originality/value – The efficiency of proposed model is demonstrated experimentally on classifying the sign language data set; in the high recognition accuracy obtained for numbers of ASL was 95.52 percent.

Journal

Kybernetes – Emerald Publishing

Published: Mar 2, 2015

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