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Proceedings Paper

An improved HMM/SVM dynamic hand gesture recognition algorithm
Author(s): Yi Zhang; Yuanyuan Yao; Yuan Luo
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Paper Abstract

In order to improve the recognition rate and stability of dynamic hand gesture recognition, for the low accuracy rate of the classical HMM algorithm in train the B parameter, this paper proposed an improved HMM/SVM dynamic gesture recognition algorithm. In the calculation of the B parameter of HMM model, this paper introduced the SVM algorithm which has the strong ability of classification. Through the sigmoid function converted the state output of the SVM into the probability and treat this probability as the observation state transition probability of the HMM model. After this, it optimized the B parameter of HMM model and improved the recognition rate of the system. At the same time, it also enhanced the accuracy and the real-time performance of the human-computer interaction. Experiments show that this algorithm has a strong robustness under the complex background environment and the varying illumination environment. The average recognition rate increased from 86.4% to 97.55%.

Paper Details

Date Published: 15 October 2015
PDF: 7 pages
Proc. SPIE 9672, AOPC 2015: Advanced Display Technology; and Micro/Nano Optical Imaging Technologies and Applications, 96720D (15 October 2015); doi: 10.1117/12.2197328
Show Author Affiliations
Yi Zhang, Chongqing Univ. of Posts and Telecommunications (China)
Yuanyuan Yao, Chongqing Univ. of Posts and Telecommunications (China)
Yuan Luo, Chongqing Univ. of Posts and Telecommunications (China)


Published in SPIE Proceedings Vol. 9672:
AOPC 2015: Advanced Display Technology; and Micro/Nano Optical Imaging Technologies and Applications
Byoungho Lee; Yikai Su; Min Gu; Xiaocong Yuan; Daniel Jaque, Editor(s)

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