CFP last date
20 May 2024
Call for Paper
June Edition
IJCA solicits high quality original research papers for the upcoming June edition of the journal. The last date of research paper submission is 20 May 2024

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

Hand Gesture Recognition using Multiclass Support Vector Machine

by Md. Hafizur Rahman, Jinia Afrin
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 74 - Number 1
Year of Publication: 2013
Authors: Md. Hafizur Rahman, Jinia Afrin
10.5120/12852-9367

Md. Hafizur Rahman, Jinia Afrin . Hand Gesture Recognition using Multiclass Support Vector Machine. International Journal of Computer Applications. 74, 1 ( July 2013), 39-43. DOI=10.5120/12852-9367

@article{ 10.5120/12852-9367,
author = { Md. Hafizur Rahman, Jinia Afrin },
title = { Hand Gesture Recognition using Multiclass Support Vector Machine },
journal = { International Journal of Computer Applications },
issue_date = { July 2013 },
volume = { 74 },
number = { 1 },
month = { July },
year = { 2013 },
issn = { 0975-8887 },
pages = { 39-43 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume74/number1/12852-9367/ },
doi = { 10.5120/12852-9367 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:41:06.604238+05:30
%A Md. Hafizur Rahman
%A Jinia Afrin
%T Hand Gesture Recognition using Multiclass Support Vector Machine
%J International Journal of Computer Applications
%@ 0975-8887
%V 74
%N 1
%P 39-43
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Vision-based recognition system has developed rapidly over the past few years. This paper presents hand gesture recognition system that can be used for interfacing between computer and human using hand gesture. In natural Human Computer Interactions (HCI), visual interpretation of gestures can be very useful. In this paper we propose a method for recognizing hand gestures using Support Vector Machine (SVM). We propose a system which can identify specific hand gestures and use them to convey information. In this system we select the feature vectors by Biorthogonal Wavelet Transform. These extracted features are used as input to the classifier. Multi Class SVM is used for classifying hand gestures into ten categories: A, B, C, D, G, H, I, L, V, Y. This system gives us good performance for recognizing the gestures. We can get up to 92% correct results on a particular gesture set.

References
  1. Andrew Wilson and Aaron Bobick, "Learning visual behavior for gesture analysis," In Proceedings of the IEEE Symposium on Computer Vision, Coral Gables, Florida, pp. 19-21, November 1995.
  2. I. E. Sketchpad: "Aman-machine graphical communication system". In: Proceedings of the AFIPS Spring Joint Computer Conference 23. pp. 329–346, 1963.
  3. X. Deyou, "A Network Approach for Hand Gesture Recognition in Virtual Reality Driving Training System of SPG", International Conference ICPR, pp. 519-522, 2006.
  4. E. Holden, R. Owens and G. Roy, "Hand Movement Classification Using Adaptive Fuzzy Expert System", Journal of Expert Systems, Vol. 9(4), pp. 465-480, 1996.
  5. M. Elmezain, A. Al-Hamadi and B. Michaelis, "Real-Time Capable System for Hand Gesture Recognition Using Hidden Markov Models in Stereo Color Image Sequences", Journal of WSCG, Vol. 16, pp. 65-72, 2008.
  6. M. Elmezain, A. Al-Hamadi and B. Michaelis, "A Novel System for Automatic Hand Gesture Spotting and Recognition in Stereo Color Image Sequences". Journal of WSCG, Vol. 17, No. 1, pp. 89-96, 2009.
  7. M. Elmezain, A. Al-Hamadi, J. Appenrodt and B. Michaelis, "A Hidden Markov Model-Based Continuous Gesture Recognition System for Hand Motion Trajectory". International Conference on Pattern Recognition (ICPR), pp. 519-522, 2008.
  8. M. Elmezain, A. Al-Hamadi and B. Michaelis, "Spatio- Temporal Feature Extraction-Based Hand Gesture Recognition for Isolated American Sign Language and Arabic Numbers". IEEE Symposium on ISPA, pp. 254-259, 2009.
  9. P. Viola and M Jones, "Rapid object detection using a boosted cascade of simple features," in Proceedings of Computer Vision and Pattern Recognition. Hawaii,U. S. , 2001, pp. 511–518.
  10. Md. Hasanuzzaman, V. Ampornaramveth, Tao Zhang, M. A. Bhuiyan ,Y. Shirai and H. Ueno, "Real-time Vision-based Gesture Recognition for Human Robot Interaction". In the Proceedings of the IEEE International Conference on Robotics and Biomimetics, Shenyang China, 2004.
  11. Y. Ho-Sub, S. Jung, J. B. Young and S. Y. Hyun, "Hand Gesture Recognition using Combined Features of Location,Angle and Velocity", Journal of Pattern Recognition, Vol. 34(7), pp. 1491-1501, 2001.
  12. Y. M. Wu, "The implementation of gesture recognition for media player system," Master Thesis of the Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan, 2009.
  13. Gregory Beylkin, "Discrete Radon Transform", Ieee Transactions On Acoustics, Speech, And Signal Processing, vol. assp-35, no. 2,1987, pp. 162-172
  14. Canny, J. , "A Computational Approach to Edge Detection", IEEE Trans. Pattern Analysis and Machine Intelligence, 8:679-714, 1986.
  15. Christopher J. C. Burges, "A Tutorial on Support Vector Machines for Pattern Recognition". Kluwer Academic Publishers, Boston, pp. 1-43
  16. Shigeo Abe, "Support Vector Machines for Pattern Classification, second edition", Kobe University,Graduate School of Engineering, 2nd edition, Springer-Verlag London Limited 2005, 2010.
  17. Stéphane G. Mallat, "A Wavelet Tour of Signal Processing". Academic Press, 1999, ISBN 978-0-12-466606-1
Index Terms

Computer Science
Information Sciences

Keywords

Gesture Recognition Canny Edge Detection Radon Transform Biorthogonal Wavelet Multiclass Support Vector Machine

Powered by PhDFocusTM