University of Surrey

Test tubes in the lab Research in the ATI Dance Research

Sign Language Recognition using Linguistically Derived Sub-Units

Cooper, H and Bowden, R (2010) Sign Language Recognition using Linguistically Derived Sub-Units In: IREC 2010, 2010-05-17 - 2010-05-23, Valetta, Malta.

Available under License : See the attached licence file.

Download (566kB)
Text (licence)

Download (33kB)


This work proposes to learn linguistically-derived sub-unit classifiers for sign language. The responses of these classifiers can be combined by Markov models, producing efficient sign-level recognition. Tracking is used to create vectors of hand positions per frame as inputs for sub-unit classifiers learnt using AdaBoost. Grid-like classifiers are built around specific elements of the tracking vector to model the placement of the hands. Comparative classifiers encode the positional relationship between the hands. Finally, binary-pattern classifiers are applied over the tracking vectors of multiple frames to describe the motion of the hands. Results for the sub-unit classifiers in isolation are presented, reaching averages over 90%. Using a simple Markov model to combine the sub-unit classifiers allows sign level classification giving an average of 63%, over a 164 sign lexicon, with no grammatical constraints.

Item Type: Conference or Workshop Item (Conference Paper)
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering > Centre for Vision Speech and Signal Processing
Authors :
Cooper, H
Bowden, R
Date : May 2010
Contributors :
ContributionNameEmailORCID Language Resources Association (ELRA),
Additional Information : © European Language Resources Association (ELRA)
Depositing User : Symplectic Elements
Date Deposited : 12 Jun 2012 15:08
Last Modified : 31 Oct 2017 14:33

Actions (login required)

View Item View Item


Downloads per month over past year

Information about this web site

© The University of Surrey, Guildford, Surrey, GU2 7XH, United Kingdom.
+44 (0)1483 300800