BMVC 2004, Kingston, 7th-9th Sept, 2004

Minimal Training, Large Lexicon, Unconstrained Sign Language Recognition
T. Kadir (University of Oxford), R. Bowden, E. J. Ong (University of
Surrey) and A. Zisserman (University of Oxford)

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.
(pdf article)