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Minimal Training, Large Lexicon, Unconstrained Sign Language Recognition

Kadir, T, Bowden, R, Ong, EJ and Zisserman, A (2004) Minimal Training, Large Lexicon, Unconstrained Sign Language Recognition In: BMVC 2004 - British Machine Vision Conference 7-9th Sept 2004, 2004-09-07 - 2004-09-09, Kingston University, London.

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This paper presents a flexible monocular system capable of recognising sign lexicons far greater in number than previous approaches. The power of the system is due to four key elements: (i) Head and hand detection based upon boosting which removes the need for temperamental colour segmentation; (ii) A body centred description of activity which overcomes issues with camera placement, calibration and user; (iii) A two stage classification in which stage I generates a high level linguistic description of activity which naturally generalises and hence reduces training; (iv) A stage II classifier bank which does not require HMMs, further reducing training requirements. The outcome of which is a system capable of running in real-time, and generating extremely high recognition rates for large lexicons with as little as a single training instance per sign. We demonstrate classification rates as high as 92% for a lexicon of 164 words with extremely low training requirements outperforming previous approaches where thousands of training examples are required.

Item Type: Conference or Workshop Item (Conference Paper)
Subjects : Computing
Divisions : Faculty of Engineering and Physical Sciences > Computing Science
Authors :
Kadir, T
Bowden, R
Ong, EJ
Zisserman, A
Date : 7 September 2004
DOI : 10.5244/C.18.96
Copyright Disclaimer : © BMVA 2004-07-30
Related URLs :
Depositing User : Symplectic Elements
Date Deposited : 26 Oct 2016 13:46
Last Modified : 31 Oct 2017 18:51

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