Sign language recognition using sub-units

Cooper, H., Ong, E.-J., Pugeault, N. and Bowden, R. (2012) Sign language recognition using sub-units. Journal of Machine Learning Research, 13, pp. 2205-2231.

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Abstract

This paper discusses sign language recognition using linguistic sub-units. It presents three types of sub-units for consideration; those learnt from appearance data as well as those inferred from both 2D or 3D tracking data. These sub-units are then combined using a sign level classifier; here, two options are presented. The first uses Markov Models to encode the temporal changes between sub-units. The second makes use of Sequential Pattern Boosting to apply discriminative feature selection at the same time as encoding temporal information. This approach is more robust to noise and performs well in signer independent tests, improving results from the 54% achieved by the Markov Chains to 76%. © 2012 Helen Cooper, Nicolas Pugeault, Eng-Jon Ong and Richard Bowden.

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Pugeault, Dr Nicolas
Authors: Cooper, H., Ong, E.-J., Pugeault, N., and Bowden, R.
College/School:College of Science and Engineering > School of Computing Science
Journal Name:Journal of Machine Learning Research
Publisher:Microtome Publishing
ISSN:1532-4435
ISSN (Online):1533-7928

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