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Enhanced Hand Gesture Recognition System

by Yogita Bhardwaj, Sukhdip Singh
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 127 - Number 4
Year of Publication: 2015
Authors: Yogita Bhardwaj, Sukhdip Singh
10.5120/ijca2015906375

Yogita Bhardwaj, Sukhdip Singh . Enhanced Hand Gesture Recognition System. International Journal of Computer Applications. 127, 4 ( October 2015), 34-36. DOI=10.5120/ijca2015906375

@article{ 10.5120/ijca2015906375,
author = { Yogita Bhardwaj, Sukhdip Singh },
title = { Enhanced Hand Gesture Recognition System },
journal = { International Journal of Computer Applications },
issue_date = { October 2015 },
volume = { 127 },
number = { 4 },
month = { October },
year = { 2015 },
issn = { 0975-8887 },
pages = { 34-36 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume127/number4/22720-2015906375/ },
doi = { 10.5120/ijca2015906375 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:19:01.365619+05:30
%A Yogita Bhardwaj
%A Sukhdip Singh
%T Enhanced Hand Gesture Recognition System
%J International Journal of Computer Applications
%@ 0975-8887
%V 127
%N 4
%P 34-36
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper implements a hand gesture recognition technique by using the SIFT based feature extraction. The matching point threshold has been calculated by using the neural network on the basis of the input gesture and the training. The paper implements the work by using the MATLAB and analyzes the results on the various sign of ASL. The system shows the accuracy of 93.3% i.e. improved by approx. &% as compared to the existing 85.9%. The system discussed in the paper is also robust as it shows the accurate results on the manipulated gestures.

References
  1. Fang, Y., Cheng, J., Wang, K., & Lu, H. (2007, August). Hand gesture recognition using fast multi-scale analysis. In Image and Graphics, 2007. ICIG 2007. Fourth International Conference on (pp. 694-698). IEEE.
  2. Moni, M. A., & Ali, A S. (2009, August). HMM based hand gesture recognition: A review on techniques and approaches. In Computer Science and Information Technology, 2009. ICCSIT 2009. 2nd IEEE International Conference on (pp. 433-437). IEEE.
  3. Alsheakhali, M., Skaik, A., Aldahdouh, M., & Alhelou, M. (2011). Hand Gesture Recognition System. Information & Communication Systems, 132.
  4. Panwar, M. (2012, February). Hand gesture recognition based on shape parameters. In Computing, Communication and Applications (ICCCA), 2012 International Conference on (pp. 1-6). IEEE.
  5. Gurjal, P., & Kunnur, K. (2012). Real Time Hand Gesture Recognition using SIFT. International Journal for Electronics and Engineering, 19-33.
  6. Abhinandan Julka, Sandeep Bhargava, (2013). A Static Hand Gesture Recognition Based on Local Contour Sequence. International Journal of Advanced Research in Computer Science and Software Engineering, Volume 3, Issue 7, July 2013 ISSN: 2277 128X.
  7. Liu, Y., Yang, Y., Wang, L., Xu, J., Qi, H., Zhao, X., ... & Wang, J. (2013, July). Image Processing and Recognition of Multiple Static Hand Gestures for Human-Computer Interaction. In Image and Graphics (ICIG), 2013 Seventh International Conference on (pp. 465-470). IEEE.
Index Terms

Computer Science
Information Sciences

Keywords

ASL Hand Gesture SIFT Neural network Threshold.

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