Volume 7 Number 5 (May 2012)
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JCP 2012 Vol.7(5): 1176-1183 ISSN: 1796-203X
doi: 10.4304/jcp.7.5.1176-1183

Fast and Robust Method for Dynamic Gesture Recognition Using Hermite Neural Network

Wensheng Li, Chunjian Deng
Zhongshan Institute, University of Electronic Science and Technology of China, Zhongshan,China
Abstract—Due to its shortcomings such as slow convergence rate and low recognition accuracy, the traditional BP neural networks perform poorly in dynamic gesture recognition, especially for online gesture training. In this paper, a novel adaptive Hermite neural networks algorithm for dynamic gesture recognition was proposed. At first, a three-layer feed-forward neural network, of which hidden layer neurons are activated by a group of Hermite orthogonal polynomial functions was constructed. Based on its special structure, a method to determine the network weights directly was introduced, and a novel algorithm to determine the optimal number of hidden nodes adaptively was proposed. Then a rapid method of fingertips tracking was put forward to get the trajectory of dynamic gesture. At last, dynamic gesture was recognized through the trained Hermite neural network. Experiment results show that Hermite neural network can enhance the speed and precision of network training, improve the learning speed and recognition accuracy, and has good robustness and generalization ability.

Index Terms—Hermite neural network, adaptive structure determination, trajectory of gesture, dynamic gesture recognition.

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Cite: Wensheng Li, Chunjian Deng, "Fast and Robust Method for Dynamic Gesture Recognition Using Hermite Neural Network," Journal of Computers vol. 7, no. 5, pp. 1176-1183, 2012.

General Information

ISSN: 1796-203X
Abbreviated Title: J.Comput.
Frequency: Monthly
Editor-in-Chief: Prof. Liansheng Tan
Executive Editor: Ms. Nina Lee
Abstracting/ Indexing: DBLP, EBSCO,  ProQuest, INSPEC, ULRICH's Periodicals Directory, WorldCat, CNKI,etc
E-mail: jcp@iap.org
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