ISSN:
1432-1769
Keywords:
Key words:Gesture recognition – Model-based tracking – Feature extraction – 3D Hopfield neural network (HNN) – Hausdorff distance
Source:
Springer Online Journal Archives 1860-2000
Topics:
Computer Science
Notes:
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.
Type of Medium:
Electronic Resource
URL:
http://dx.doi.org/10.1007/s001380050080
Permalink
