Paper Info
Title
Sign language recognition using model-based tracking and a 3D Hopfield neural network
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.
Year
DOI
Venue
1998
10.1007/s001380050080
Mach. Vis. Appl.
Keywords
Field
DocType
sign language recognition,hopfield neural network,model-based tracking,graph matching,neural networks,computational geometry,feature extraction,gesture recognition,computer vision,hausdorff distance
Computer vision,Pattern recognition,Computer science,Gesture,Computational geometry,Gesture recognition,Feature extraction,Matching (graph theory),Sign language,Artificial intelligence,Hausdorff distance,Artificial neural network
Journal
Volume
Issue
ISSN
10
5-6
0932-8092
Citations 
PageRank 
References 
40
2.27
0
Authors
2
Name
Order
Citations
PageRank
Chung-Lin Huang154037.61
Wen-Yi Huang2402.27