Person-Independent 3D Sign Language Recognition

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.

• Type: Conference
• Year: 2007
• Where: GW
• Authors: Jeroen Lichtenauer, Gineke A. ten Holt, Marcel J. T. Reinders, Emile A. Hendriks
• Keywords: Biometrics, False Positive Rate, GW 2007, Positive Rate, True Positive Rate