PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Sign Language Recognition using Linguistically Derived Sub-Units
Helen Cooper and Richard Bowden
In: Representation and Processing of Sign Languages : Corpora and Sign Languages Technologies, Valetta, Malta(2010).

Abstract

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 classifiation giving an average of 63\%, over a 164 sign lexicon, with no grammatical constraints.

EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
ID Code:9007
Deposited By:Helen Cooper
Deposited On:21 February 2012