Abstract

The basic motivation behind this research work is to assist enormous number of disable people to enhance their capabilities regardless of their disability. In particular, we have focused and addressed the deafness and dumbness disability in human beings on technological basis. The aim was to design a system to help such people. Sign language has been used as our database in whole recognition process. The gestures have been read by comparison with available signs in the database. The work is comprised of three major parts. 1) Acquiring images in real time environment through any imaging device. 2) Recognition of those images on the basis of probability by comparing with the database. 3) Finally translating recognized images into possible output. We have used various algorithms to validate the approach and to check the efficiency. In particular, mainly adaboost and Support Vector Machine (SVM) algorithms have been tested. Both of these algorithms worked well but SVM was found to be optimum with respect to time efficiency as compared with adaboost.

Keywords:

Adaboost, database, hand posture recognition, skin detection, SVM,

References

1. Ghotkar, A.S. and G.K. Kharate, 2012. Hand segmentation techniques to hand gesture recognition for natural human computer interaction. Int. J. Human Comp. Interac., 3(1): 15.
   
2. Haider, W., M.S. Malik, M. Raza, A. Wahab, I.A. Khan, U. Zia and H. Bashir, 2012. A hybrid method for edge continuity based on Pixel Neighbors Pattern Analysis (PNPA) for remote sensing satellite images. Int. J. Commun. Netw. Syst. Sci., 5(29): 624-630.
   CrossRef    
3. Hassanpour, R. and A. Shahbahrami, 2009. Human computer interaction using vision-based hand Gesture recognition. J. Comput. Eng., 1: 21-30.
   
4. Messer, T., 2009. Static Hand Gesture Recognition. University of Fribourg, Switzerland.
   
5. Pansare, J.R., S.H. Gawande and M. Ingle, 2012. Real-time static hand gesture recognition for American Sign Language (ASL) in complex background. J. Signal Inform. Process., 3: 364-367.
   CrossRef    
6. Rautaray, S.S. and A. Agrawal, 2012. Real time multiple hand gesture recognition system for human computer interaction. Int. J. Intell. Syst. Appl., 4(5): 56.
   CrossRef    
7. Rokade, R., D. Doye and M. Kokare, 2009. Hand gesture recognition by thinning method. Proceeding of the International Conference on Digital Image Processing, pp: 284-287.
   CrossRef    
8. Sharif, M., S. Mohsin and M.Y. Javed, 2011. Real time face detection using skin detection (Block Approach). J. Appl. Comp. Sci. Math. Suceava, 10(5).
   
9. Sharif, M., M.Y. Javed and S. Mohsin, 2012. Face recognition based on facial features. Res. J. Appl. Sci., 4(17): 2879-2886.
   

Competing interests

The authors have no competing interests.

Open Access Policy

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Copyright

The authors have no competing interests.