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Advances in Human-Computer Interaction
Volume 2016, Article ID 6727806, 10 pages
Research Article

Lower Order Krawtchouk Moment-Based Feature-Set for Hand Gesture Recognition

Department of Electronics and Communication Engineering, University Institute of Engineering and Technology, Panjab University, Sector 25, Chandigarh 160036, India

Received 17 October 2015; Revised 4 February 2016; Accepted 18 February 2016

Academic Editor: Marco Mamei

Copyright © 2016 Bineet Kaur and Garima Joshi. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The capability of lower order Krawtchouk moment-based shape features has been analyzed. The behaviour of 1D and 2D Krawtchouk polynomials at lower orders is observed by varying Region of Interest (ROI). The paper measures the effectiveness of shape recognition capability of 2D Krawtchouk features at lower orders on the basis of Jochen-Triesch’s database and hand gesture database of 10 Indian Sign Language (ISL) alphabets. Comparison of original and reduced feature-set is also done. Experimental results demonstrate that the reduced feature dimensionality gives competent accuracy as compared to the original feature-set for all the proposed classifiers. Thus, the Krawtchouk moment-based features prove to be effective in terms of shape recognition capability at lower orders.