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2019 | OriginalPaper | Chapter

Static Gesture Recognition Method Based on 3D Human Hand Joints

Authors : Jingjing Gao, Yinwei Zhan

Published in: E-Learning and Games

Publisher: Springer International Publishing

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Abstract

Depth cameras support working in a dark environment, and provide depth information from objects to cameras, hence have advantages over color cameras. So in this paper we adopt depth cameras to collect accurate gesture information for 3D modeling, in order to obtain accurate gesture recognition. On the depth map, we present methods of hand joint segmentation with random forest pixel classification and of gesture recognition with template matching, which provides accurate judgment for static gestures. Rotation may occur while the acquisition of hand data, so we conduct rotation correction by using SVD decomposition. Experimental results illustrate that this method provides more accurate joint segmentation, which is robust to hand rotation and achieves a recognition rate of 94.8% on ASL dataset.

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Title: Static Gesture Recognition Method Based on 3D Human Hand Joints
Authors: Jingjing Gao
Yinwei Zhan
Publisher: Springer International Publishing
Book: E-Learning and Games
Print ISBN: 978-3-030-23711-0

Electronic ISBN: 978-3-030-23712-7

Copyright Year: 2019
DOI: https://doi.org/10.1007/978-3-030-23712-7_49

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