ABSTRACT
The South African Sign Language research group at the University of the Western Cape has created several systems to recognize Sign Language gestures using single parameters. Research has shown that five parameters are required to recognize any sign language gesture: hand shape, location, orientation and motion, as well as facial expressions. Using a single parameter can cause conflicts in recognition of signs that are similarly signed. This paper pioneers research at the group towards combining multiple parameters to better distinguish between similar signs. This eventually aims to enable the recognition of a large SASL vocabulary. The proposed methodology combines hand location and hand shape recognition into one combined recognition system. The recognition approach is applied to 12 SASL signs that consist of six pairs of signs with the same hand shape performed at two different locations. It is shown that the approach is able to achieve a high average recognition accuracy of 79% across all signs and distinguish between the signs effectively. It is also shown to be robust to variations in test subjects.
- W. C. Stokoe. Sign language structure: An outline of the visual communication systems of the american deaf. Journal of Deaf studies and Deaf education, 10(34):3--37, 2005.Google Scholar
- D. Mushfieldt et al. Robust Facial Expression Recognition in the Presence of Rotation and Partial Occlusion. 2013.Google Scholar
- J. Whitehill. Automatic Real-time Facial Expression Recognition for Signed Language Translation. Master's thesis, University of the Western Cape, Computer Science, 2006.Google Scholar
- M. Sheikh. Robust Recognition of Facial Expressions on Noise Degraded Facial Images. Master's thesis, University of the Western Cape, Computer Science, 2011.Google Scholar
- P. Li. Hand shape estimation for South African Sign Language. Master's thesis, University of the Western Cape, Computer Science, 2010.Google Scholar
- V. Segers. The efficacy of the eigenvector approach to south african sign language identification. In South African Telecommunication Networks and Applications Conference, pages 363--366, 2009.Google Scholar
- I. Achmed. Upper body pose recognition and estimation towards the translation of South African Sign Language. Master's thesis, University of the Western Cape, Computer Science, 2010.Google Scholar
- D. Brown. Faster Upper Body Pose Estimation and Recognition using CUDA. Master's thesis, University of the Western Cape, Computer Science, 2012.Google Scholar
- C. Rajah. Chereme-based recognition of isolated, dynamic gestures from South African Sign Language with Hidden Markov Models. Master's thesis, University of the Western Cape, Computer Science, 2006.Google Scholar
- N. Naidoo. South African Sign Language recognition using feature vectors and Hidden Markov Models. Master's thesis, University of the Western Cape, Computer Science, 2009.Google Scholar
- Rung-Huei Liang and Ming Ouhyoung. A real-time continuous gesture recognition system for sign language. In Automatic Face and Gesture Recognition, 1998. Proceedings. Third IEEE International Conference on, pages 558--567, 1998. Google ScholarDigital Library
- A. Adams P. Vamplew. Recognition of Sign Language Gestures using neural netorks. PhD thesis, University, 1996.Google Scholar
- Feng-Sheng Chen, Chih-Ming Fu, and Chung-Lin Huang. Hand gesture recognition using a real-time tracking method and hidden markov models. Image and Vision Computing, 21(8):745--758, 2003.Google ScholarCross Ref
- J. J. Weng Y. Cui. Hand sign recognition from intensity image sequences with complex backgrounds. In Proceedings IEEE Second International Conference on Automatic Face and Gesture Recognition, 1996. Google ScholarDigital Library
- P. Viola and M. Jones. Rapid object detection using a boosted cascade of simple features. In Proceedings of the IEEE Computer Society International Conference on Computer Vision and Pattern Recognition, pages 511--518, 2001.Google ScholarCross Ref
- A. Salhi and Y. Jammoussi. Object tracking using camshift, meanshift, and kalman filter. In World Academy of Science, Engineering and Technology, volume 64, 2012.Google Scholar
- L. Yi and J. Connan. Kerntune: Self-tuning linux kernel performance using support vector machines. In Proceedings of the 2007 Annual Research Conference of the South Africa Institute of Computer Scientists and Information Technologists on IT Research in Developing Countries, pages 189--196, 2007. Google ScholarDigital Library
- S. Howard. Finger Talk: South African Sign Language dictionary. 2008.Google Scholar
Index Terms
- An integrated sign language recognition system
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