Vision Based Hand Gesture Recognition Using 3D Shape Context

Chen Zhu; Jianyu Yang; Zhanpeng Shao; Youfu Li

doi:10.1109/ROBIO.2018.8665050

Vision Based Hand Gesture Recognition Using 3D Shape Context

Chen Zhu, Jianyu Yang^*, Zhanpeng Shao, Youfu Li

^*Corresponding author for this work

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

1 Citation (Scopus)

Abstract

Hand gesture recognition plays an important role in robot vision and makes human-robot interaction more flexible and convenient. Among the hand gesture features, shape is a meaningful and informative cue and the effective representation of hand shape is critical for recognition. In this paper, we propose a novel method to capture the shape information of 3D hand gestures. Hand shapes are segmented from the depth images which are captured by the Kinect sensor with cluttered backgrounds. Given the surface of the hand shape, we construct vectors and build histograms based on the vector division. Then a hand gesture is represented by a 3D Shape Context descriptor with rich 3D information. The Dynamic Time Warping algorithm is finally used for hand gesture recognition. Extensive experiments on two benchmark datasets are conducted to test the proposed method and the experimental results verify that the proposed method outperforms the recent related methods.

Original language	English
Title of host publication	2018 IEEE International Conference on Robotics and Biomimetics (ROBIO)
Publisher	IEEE
Pages	784-789
ISBN (Electronic)	9781728103778, 9781728103761
ISBN (Print)	9781728103785
DOIs	https://doi.org/10.1109/ROBIO.2018.8665050
Publication status	Published - Dec 2018
Event	2018 IEEE International Conference on Robotics and Biomimetics (IEEE ROBIO 2018) - Kuala Lumpur, Malaysia Duration: 12 Dec 2018 → 15 Dec 2018 Conference number: 15th http://www.robio2018.org/

Publication series

Name	IEEE International Conference on Robotics and Biomimetics, ROBIO

Conference

Conference	2018 IEEE International Conference on Robotics and Biomimetics (IEEE ROBIO 2018)
Abbreviated title	IEEE ROBIO 2018
Place	Malaysia
City	Kuala Lumpur
Period	12/12/18 → 15/12/18
Internet address	http://www.robio2018.org/

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@inproceedings{dfabe1975d9b4026a3d618809d421fcf,

title = "Vision Based Hand Gesture Recognition Using 3D Shape Context",

abstract = "Hand gesture recognition plays an important role in robot vision and makes human-robot interaction more flexible and convenient. Among the hand gesture features, shape is a meaningful and informative cue and the effective representation of hand shape is critical for recognition. In this paper, we propose a novel method to capture the shape information of 3D hand gestures. Hand shapes are segmented from the depth images which are captured by the Kinect sensor with cluttered backgrounds. Given the surface of the hand shape, we construct vectors and build histograms based on the vector division. Then a hand gesture is represented by a 3D Shape Context descriptor with rich 3D information. The Dynamic Time Warping algorithm is finally used for hand gesture recognition. Extensive experiments on two benchmark datasets are conducted to test the proposed method and the experimental results verify that the proposed method outperforms the recent related methods.",

author = "Chen Zhu and Jianyu Yang and Zhanpeng Shao and Youfu Li",

year = "2018",

month = dec,

doi = "10.1109/ROBIO.2018.8665050",

language = "English",

isbn = "9781728103785",

series = "IEEE International Conference on Robotics and Biomimetics, ROBIO",

publisher = "IEEE",

pages = "784--789",

booktitle = "2018 IEEE International Conference on Robotics and Biomimetics (ROBIO)",

address = "United States",

note = "2018 IEEE International Conference on Robotics and Biomimetics (IEEE ROBIO 2018), IEEE ROBIO 2018 ; Conference date: 12-12-2018 Through 15-12-2018",

url = "http://www.robio2018.org/",

}

Zhu, C, Yang, J, Shao, Z & Li, Y 2018, Vision Based Hand Gesture Recognition Using 3D Shape Context. in 2018 IEEE International Conference on Robotics and Biomimetics (ROBIO)., 8665050, IEEE International Conference on Robotics and Biomimetics, ROBIO, IEEE, pp. 784-789, 2018 IEEE International Conference on Robotics and Biomimetics (IEEE ROBIO 2018), Kuala Lumpur, Malaysia, 12/12/18. https://doi.org/10.1109/ROBIO.2018.8665050

Vision Based Hand Gesture Recognition Using 3D Shape Context. / Zhu, Chen; Yang, Jianyu; Shao, Zhanpeng et al.
2018 IEEE International Conference on Robotics and Biomimetics (ROBIO). IEEE, 2018. p. 784-789 8665050 (IEEE International Conference on Robotics and Biomimetics, ROBIO).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - Vision Based Hand Gesture Recognition Using 3D Shape Context

AU - Zhu, Chen

AU - Yang, Jianyu

AU - Shao, Zhanpeng

AU - Li, Youfu

N1 - Conference code: 15th

PY - 2018/12

Y1 - 2018/12

N2 - Hand gesture recognition plays an important role in robot vision and makes human-robot interaction more flexible and convenient. Among the hand gesture features, shape is a meaningful and informative cue and the effective representation of hand shape is critical for recognition. In this paper, we propose a novel method to capture the shape information of 3D hand gestures. Hand shapes are segmented from the depth images which are captured by the Kinect sensor with cluttered backgrounds. Given the surface of the hand shape, we construct vectors and build histograms based on the vector division. Then a hand gesture is represented by a 3D Shape Context descriptor with rich 3D information. The Dynamic Time Warping algorithm is finally used for hand gesture recognition. Extensive experiments on two benchmark datasets are conducted to test the proposed method and the experimental results verify that the proposed method outperforms the recent related methods.

AB - Hand gesture recognition plays an important role in robot vision and makes human-robot interaction more flexible and convenient. Among the hand gesture features, shape is a meaningful and informative cue and the effective representation of hand shape is critical for recognition. In this paper, we propose a novel method to capture the shape information of 3D hand gestures. Hand shapes are segmented from the depth images which are captured by the Kinect sensor with cluttered backgrounds. Given the surface of the hand shape, we construct vectors and build histograms based on the vector division. Then a hand gesture is represented by a 3D Shape Context descriptor with rich 3D information. The Dynamic Time Warping algorithm is finally used for hand gesture recognition. Extensive experiments on two benchmark datasets are conducted to test the proposed method and the experimental results verify that the proposed method outperforms the recent related methods.

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U2 - 10.1109/ROBIO.2018.8665050

DO - 10.1109/ROBIO.2018.8665050

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 9781728103785

T3 - IEEE International Conference on Robotics and Biomimetics, ROBIO

SP - 784

EP - 789

BT - 2018 IEEE International Conference on Robotics and Biomimetics (ROBIO)

PB - IEEE

T2 - 2018 IEEE International Conference on Robotics and Biomimetics (IEEE ROBIO 2018)

Y2 - 12 December 2018 through 15 December 2018

ER -

Vision Based Hand Gesture Recognition Using 3D Shape Context

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