Real-Time Static Hand Gesture Recognition for American Sign Language (ASL) in Complex Background

Abstract Full-Text HTML Download Download as PDF (Size:307KB) PP. 364-367
DOI: 10.4236/jsip.2012.33047    6,697 Downloads   12,406 Views   Citations

ABSTRACT

Hand gestures are powerful means of communication among humans and sign language is the most natural and expressive way of communication for dump and deaf people. In this work, real-time hand gesture system is proposed. Experimental setup of the system uses fixed position low-cost web camera with 10 mega pixel resolution mounted on the top of monitor of computer which captures snapshot using Red Green Blue [RGB] color space from fixed distance. This work is divided into four stages such as image preprocessing, region extraction, feature extraction, feature matching. First stage converts captured RGB image into binary image using gray threshold method with noise removed using median filter [medfilt2] and Guassian filter, followed by morphological operations. Second stage extracts hand region using blob and crop is applied for getting region of interest and then “Sobel” edge detection is applied on extracted region. Third stage produces feature vector as centroid and area of edge, which will be compared with feature vectors of a training dataset of gestures using Euclidian distance in the fourth stage. Least Euclidian distance gives recognition of perfect matching gesture for display of ASL alphabet, meaningful words using file handling. This paper includes experiments for 26 static hand gestures related to A-Z alphabets. Training dataset consists of 100 samples of each ASL symbol in different lightning conditions, different sizes and shapes of hand. This gesture recognition system can reliably recognize single-hand gestures in real time and can achieve a 90.19% recognition rate in complex background with a “minimum-possible constraints” approach.

Cite this paper

J. Pansare, S. Gawande and M. Ingle, "Real-Time Static Hand Gesture Recognition for American Sign Language (ASL) in Complex Background," Journal of Signal and Information Processing, Vol. 3 No. 3, 2012, pp. 364-367. doi: 10.4236/jsip.2012.33047.

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1] Q. Y. Zhang, F. Chen and X. W. Liu, “Hand Gesture Detection and Segmentation Based on Difference Background Image with Complex Background,” Proceedings of the 2008 International Conference on Embedded Software and Systems, Sichuan, 29-31 July 2008, pp. 338-343.
[2] J.-S. Lee, Y.-J. Lee, E.-H. Lee and S.-H. Hong, “Hand Region Extraction and Gesture Recognition from Video Stream with Complex Background through Entropy Analysis,” Proceedings of the 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, San Francisco, 1-5 September 2004, pp. 1513-1516.
[3] R. Rokade, D. Doye and M. Kokare, “Hand Gesture Recognition by Thinning Method,” International Conference on Digital Image Processing, Bangkok, 7-9 March 2009, pp. 284-287. doi:10.1109/ICDIP.2009.73
[4] Y. K. Fang, K. Q. Wang, J. Cheng and H. Q. Lu, “A Real-Time Hand Gesture Recognition Method,” Proceedings of IEEE International Conference on Multimedia and Expo, Beijing, 2-5 July 2007, pp. 995-998.
[5] T. Messer, “Static Hand Gesture Recognition,” University of Fribourg, Switzerland, 2009.
[6] W.-K. Chung, X. Y. Wu and Y. S. Xu, “A Realtime Hand Gesture Recognition Based on Haar Wavelet Representation,” Proceedings of the 2008 IEEE International Conference on Robotics and Biometrics, Bangkok, 22-25 February 2009, pp. 336-341. doi:10.1109/ROBIO.2009.4913026
[7] U. S. Rokade, D. L. Doye and M. Kokare, “Hand Gesture Recognition Using Object Based Key Frame Selection,” Proceedings of International Conference on Digital Image Processing, Bangkok, 7-9 March 2009, pp. 288-291. doi:10.1109/ICDIP.2009.74
[8] Y. Ren and F. M. Zhang, “Hand Gesture Recognition Based on MEB-SVM,” Proceedings of the 2009 International Conference on Embedded Software and Systems, Zhejiang, 25-27 May 2009, pp. 344-349. doi:10.1109/ICESS.2009.21
[9] Z. Q. Feng, P. L. Du, X. N. Song, Z. X. Chen, T. Xu and D. L. Zhu, “Research on Features Extraction From Frame Image Sequences,” Proceedings of 2008 International Symposium on Computer Science and Computational Tech- nology, Shanghai, 20-22 December 2008, pp.762-766.
[10] L. W. Howe, F. Wong and A. Chekima, “Comparison of Hand Segmentation Methodologies for Hand Gesture Recognition,” Proceedings of International Symposium on Information Technology, Kuala Lumpur, 26-28 August 2008, pp. 1-7. doi:10.1109/ITSIM.2008.4631669

  
comments powered by Disqus

Copyright © 2020 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.