Person-Independent 3D Sign Language Recognition

Authors:
Jeroen F. Lichtenauer;Gineke A. Holt;Marcel J. Reinders;Emile A. Hendriks
Affiliations:
Information and Communication Theory Group, Delft University of Technology, Delft, The Netherlands 2628 CD;Information and Communication Theory Group, Delft University of Technology, Delft, The Netherlands 2628 CD and Human Information Communication Design, Delft University of Technology, Delft, The Ne ...;Information and Communication Theory Group, Delft University of Technology, Delft, The Netherlands 2628 CD;Information and Communication Theory Group, Delft University of Technology, Delft, The Netherlands 2628 CD
Venue:
Gesture-Based Human-Computer Interaction and Simulation
Year:
2009

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Abstract

In this paper, we present a person independent 3D system for judging the correctness of a sign. The system is camera-based, using computer vision techniques to track the hand and extract features. 3D co-ordinates of the hands and other features are calculated from stereo images. The features are then modeled statistically and automatic feature selection is used to build the classifiers. Each classifier is meant to judge the correctness of one sign. We tested our approach using a 120-sign vocabulary and 75 different signers. Overall, a true positive rate of 96.5% at a false positive rate of 3.5% is achieved. The system's performance in a real-world setting largely agreed with human expert judgement.