Abstract
In view of the influence of complex and changeable gestures on recognition, a gesture recognition method based on multi feature phase fusion is proposed. Firstly, the skeleton feature and contour feature of the gesture area are extracted. Then the feature fusion method is used to obtain the fusion features of the gestures. Finally, support vector machine, decision tree, random forest and convolution neural network are used to recognize the skeleton feature, contour feature and fusion feature of gesture area respectively. The results show that under different data sets, gesture recognition based on multi feature fusion improves the recognition accuracy by 2% compared with single feature recognition algorithm, reaching 98.57%.
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Acknowledgments
This research work is supported by Innovation Project of Guangxi University for Nationalities Graduate Education (gxun-chxzs2017112); National Natural Science Fund (21466008, 21566007); Guangxi Natural Science Foundation (2015GXNSFAA13911).
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Yang, H., Xuan, S., Mo, Y. (2018). Hand Gesture Recognition Based on Multi Feature Fusion. In: Tan, Y., Shi, Y., Tang, Q. (eds) Advances in Swarm Intelligence. ICSI 2018. Lecture Notes in Computer Science(), vol 10942. Springer, Cham. https://doi.org/10.1007/978-3-319-93818-9_37
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DOI: https://doi.org/10.1007/978-3-319-93818-9_37
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