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Real-time Hand Gesture Recognition from Depth Images Using Convex Shape Decomposition Method

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Abstract

Hand gesture recognition is one of the most natural and intuitive ways to communicate between people and machines, since it closely mimics how human interact with each other. This paper presents a novel method for real-time markerless hand gesture recognition from depth images. The proposed method encompasses a collection of techniques that enable the detection, segmentation and recognition of hand gestures. A Hand detection and location method is employed using the depth information acquired from a depth sensor. Then, the hand is robustly segmented in cluttered background without any marker around. A convex shape decomposition method based on Radius Morse function is proposed for hand shape decomposition in real-time. Hand palm, fingertips and hand skeleton are recognized based on the hand shape decomposition and hand features. Moreover, we present a method for recognition of two-hand gestures. Representative experimental results demonstrate qualitatively and quantitatively that accurate hand gesture recognition can be achieved for real-time applications.

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Acknowledgments

This work is supported by CASIA - Beijing CAS X-Vision Digital Technology Co., Ltd. Joint Lab of Information Visualization.

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Authors and Affiliations

  1. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Shuxin Qin, Xiaoyang Zhu, Yiping Yang & Yongshi Jiang

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  1. Shuxin Qin
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  2. Xiaoyang Zhu
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  3. Yiping Yang
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  4. Yongshi Jiang
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Correspondence to Shuxin Qin.

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Qin, S., Zhu, X., Yang, Y. et al. Real-time Hand Gesture Recognition from Depth Images Using Convex Shape Decomposition Method. J Sign Process Syst 74, 47–58 (2014). https://doi.org/10.1007/s11265-013-0778-7

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  • DOI: https://doi.org/10.1007/s11265-013-0778-7

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