Computer Science ›› 2016, Vol. 43 ›› Issue (Z11): 247-251.doi: 10.11896/j.issn.1002-137X.2016.11A.057

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HMM Static Gesture Recognition Algorithm Based on Fusing Local Feature and Global Feature

ZHANG Li-zhi, HUANG Ju, SUN Hua-dong, ZHAO Zhi-jie, CHEN Li and XING Zong-xin   

  • Online:2018-12-01 Published:2018-12-01

Abstract: Focusing on the issue of static gesture recognition, a hidden markov model (HMM) static gesture recognition algorithm based on local and global contour shape was proposed.It extracts local features and upper contour shape entropy as training data of each type of gesture respectively to train its HMM parameters.While testing,the algorithm works with local shape entropy to obtain preliminary identification results,and then according to the fuzziness of prelimi-nary identification,chooses whether it needs to work with upper contour feature,which is a kind of global characteristic,and complementary to the local characteristic to get the final result.The experimental results show that the algorithm has a good effect for gesture library in which shape difference is dominant.And the ideal simulating static spatial feature data into time series makes static gesture recognition have the space scale invariance.At the same time,reasonable data dimension has shortened the training time,and accelerated the speed of recognition.

Key words: Static gesture recognition,HMM,Shape entropy,Upper contour feature

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