Sign language recognition using model-based tracking and a 3D Hopfield neural network

This paper presents a sign language recognition system which consists of three modules: model-based hand tracking, feature extraction, and gesture recognition using a 3D Hopfield neural network (HNN). The first one uses the Hausdorff distance measure to track shape-variant hand motion, the second one applies the scale and rotation-invariant Fourier descriptor to characterize hand figures, and the last one performs a graph matching between the input gesture model and the stored models by using a 3D modified HNN to recognize the gesture. Our system tests 15 different hand gestures. The experimental results show that our system can achieve above 91 % recognition rate, and the recognition process time is about 10 s. The major contribution in this paper is that we propose a 3D modified HNN for gesture recognition which is more reliable than the conventional methods.