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.
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|
|Date :||May 2010|
|Additional Information :||© European Language Resources Association (ELRA)|
|Depositing User :||Symplectic Elements|
|Date Deposited :||12 Jun 2012 15:08|
|Last Modified :||09 Jun 2014 13:18|
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