MaHG-RGBD: A Multi-angle View Hand Gesture RGB-D Dataset for Deep Learning Based Gesture Recognition and Baseline Evaluations

Jia Qing Liu; Kotaro Furusawa; Seiju Tsujinaga; Tomoko Tateyama; Yutaro Iwamoto; Yen Wei Chen

doi:10.1109/ICCE.2019.8661941

MaHG-RGBD: A Multi-angle View Hand Gesture RGB-D Dataset for Deep Learning Based Gesture Recognition and Baseline Evaluations

Jia Qing Liu, Kotaro Furusawa, Seiju Tsujinaga, Tomoko Tateyama, Yutaro Iwamoto, Yen Wei Chen

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

7 Citations (Scopus)

Abstract

Here, we present a new dataset, named MaHG-RGBD, including 25 hand gestures performed by 15 participants as viewed from multiple angles. This dataset is intended to train models for deep-learning-based hand-gesture recognition. Unlike existing datasets, MaHG-RGBD includes not only front views (tilt angle = 0) but also the titled views (tilt angle = 45 degrees), which are often needed when there are space constraints. In addition, the dataset includes pairs of synchronized color and depth images of the hand region that are well segmented. Users can utilize just one of the image modalities or both depending on the application. This dataset includes a wide variety of different gestures classes: a total of 25 hand gestures. We evaluate the recognition accuracy of 25 different hand gestures using deep learning as a benchmark with this dataset. The MaHG-RGBD dataset is available at http://www.iipl.is.ritsumei.ac.jp/MaHG-RGBD.

Original language	English
Title of host publication	2019 IEEE International Conference on Consumer Electronics, ICCE 2019
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781538679104
DOIs	https://doi.org/10.1109/ICCE.2019.8661941
Publication status	Published - 06-03-2019
Externally published	Yes
Event	2019 IEEE International Conference on Consumer Electronics, ICCE 2019 - Las Vegas, United States Duration: 11-01-2019 → 13-01-2019

Publication series

Name	2019 IEEE International Conference on Consumer Electronics, ICCE 2019

Conference

Conference	2019 IEEE International Conference on Consumer Electronics, ICCE 2019
Country/Territory	United States
City	Las Vegas
Period	11-01-19 → 13-01-19

All Science Journal Classification (ASJC) codes

Industrial and Manufacturing Engineering
Media Technology
Electrical and Electronic Engineering

Access to Document

10.1109/ICCE.2019.8661941

Cite this

Liu, J. Q., Furusawa, K., Tsujinaga, S., Tateyama, T., Iwamoto, Y., & Chen, Y. W. (2019). MaHG-RGBD: A Multi-angle View Hand Gesture RGB-D Dataset for Deep Learning Based Gesture Recognition and Baseline Evaluations. In 2019 IEEE International Conference on Consumer Electronics, ICCE 2019 Article 8661941 (2019 IEEE International Conference on Consumer Electronics, ICCE 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCE.2019.8661941

Liu, Jia Qing ; Furusawa, Kotaro ; Tsujinaga, Seiju et al. / MaHG-RGBD : A Multi-angle View Hand Gesture RGB-D Dataset for Deep Learning Based Gesture Recognition and Baseline Evaluations. 2019 IEEE International Conference on Consumer Electronics, ICCE 2019. Institute of Electrical and Electronics Engineers Inc., 2019. (2019 IEEE International Conference on Consumer Electronics, ICCE 2019).

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title = "MaHG-RGBD: A Multi-angle View Hand Gesture RGB-D Dataset for Deep Learning Based Gesture Recognition and Baseline Evaluations",

abstract = "Here, we present a new dataset, named MaHG-RGBD, including 25 hand gestures performed by 15 participants as viewed from multiple angles. This dataset is intended to train models for deep-learning-based hand-gesture recognition. Unlike existing datasets, MaHG-RGBD includes not only front views (tilt angle = 0) but also the titled views (tilt angle = 45 degrees), which are often needed when there are space constraints. In addition, the dataset includes pairs of synchronized color and depth images of the hand region that are well segmented. Users can utilize just one of the image modalities or both depending on the application. This dataset includes a wide variety of different gestures classes: a total of 25 hand gestures. We evaluate the recognition accuracy of 25 different hand gestures using deep learning as a benchmark with this dataset. The MaHG-RGBD dataset is available at http://www.iipl.is.ritsumei.ac.jp/MaHG-RGBD.",

author = "Liu, {Jia Qing} and Kotaro Furusawa and Seiju Tsujinaga and Tomoko Tateyama and Yutaro Iwamoto and Chen, {Yen Wei}",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE International Conference on Consumer Electronics, ICCE 2019 ; Conference date: 11-01-2019 Through 13-01-2019",

year = "2019",

month = mar,

day = "6",

doi = "10.1109/ICCE.2019.8661941",

language = "English",

series = "2019 IEEE International Conference on Consumer Electronics, ICCE 2019",

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Liu, JQ, Furusawa, K, Tsujinaga, S, Tateyama, T, Iwamoto, Y & Chen, YW 2019, MaHG-RGBD: A Multi-angle View Hand Gesture RGB-D Dataset for Deep Learning Based Gesture Recognition and Baseline Evaluations. in 2019 IEEE International Conference on Consumer Electronics, ICCE 2019., 8661941, 2019 IEEE International Conference on Consumer Electronics, ICCE 2019, Institute of Electrical and Electronics Engineers Inc., 2019 IEEE International Conference on Consumer Electronics, ICCE 2019, Las Vegas, United States, 11-01-19. https://doi.org/10.1109/ICCE.2019.8661941

MaHG-RGBD: A Multi-angle View Hand Gesture RGB-D Dataset for Deep Learning Based Gesture Recognition and Baseline Evaluations. / Liu, Jia Qing; Furusawa, Kotaro; Tsujinaga, Seiju et al.
2019 IEEE International Conference on Consumer Electronics, ICCE 2019. Institute of Electrical and Electronics Engineers Inc., 2019. 8661941 (2019 IEEE International Conference on Consumer Electronics, ICCE 2019).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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Liu JQ, Furusawa K, Tsujinaga S, Tateyama T, Iwamoto Y, Chen YW. MaHG-RGBD: A Multi-angle View Hand Gesture RGB-D Dataset for Deep Learning Based Gesture Recognition and Baseline Evaluations. In 2019 IEEE International Conference on Consumer Electronics, ICCE 2019. Institute of Electrical and Electronics Engineers Inc. 2019. 8661941. (2019 IEEE International Conference on Consumer Electronics, ICCE 2019). doi: 10.1109/ICCE.2019.8661941

MaHG-RGBD: A Multi-angle View Hand Gesture RGB-D Dataset for Deep Learning Based Gesture Recognition and Baseline Evaluations

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