Abstract.
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.
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Received: 13 September 1996 / Accepted: 28 October 1997
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Huang, CL., Huang, WY. Sign language recognition using model-based tracking and a 3D Hopfield neural network. Machine Vision and Applications 10, 292–307 (1998). https://doi.org/10.1007/s001380050080
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DOI: https://doi.org/10.1007/s001380050080