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Hand Part Classification Using Single Depth Images

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Computer Vision - ACCV 2014 Workshops (ACCV 2014)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9009))

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Abstract

Hand pose recognition has received increasing attention as an area of HCI. Recently with the spreading of many low cost 3D camera, researches for understanding more natural gestures have been studied. In this paper we present a method for hand part classification and joint estimation from a single depth image. We apply random decision forests(RDF) for hand part classification. Foreground pixels in the hand image are estimated by RDF, which is called per-pixel classification. Then hand joints are estimated based on the classified hand parts. We suggest robust feature extraction method for per-pixel classification, which enhances the accuracy of hand part classification. Depth images and label images synthesized by 3D hand mesh model are used for algorithm verification. Finally we apply our algorithm to the real depth image from conventional 3D camera and show the experiment result.

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Acknowledgement

This work was supported by the DGIST R&D Program of the Ministry of Education, Science and Technology of Korea (14-IT-03). It was also supported by Ministry of Culture, Sports and Tourism (MCST) and Korea Creative Content Agency (KOCCA) in the Culture Technology (CT) Research & Development Program (Immersive Game Contents CT Co-Research Center).

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Authors and Affiliations

  1. Department of IT Convergence, Daegu Gyeongbuk Institute of Science and Technology (DGIST), Daegu, South Korea

    Myoung-Kyu Sohn, Dong-Ju Kim & Hyunduk Kim

Authors
  1. Myoung-Kyu Sohn
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  2. Dong-Ju Kim
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  3. Hyunduk Kim
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Corresponding author

Correspondence to Myoung-Kyu Sohn .

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Editors and Affiliations

  1. Center for Visual Information Technology, International Institute of Information Technology, Hyderabad, India

    C.V. Jawahar

  2. Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

    Shiguang Shan

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Sohn, MK., Kim, DJ., Kim, H. (2015). Hand Part Classification Using Single Depth Images. In: Jawahar, C., Shan, S. (eds) Computer Vision - ACCV 2014 Workshops. ACCV 2014. Lecture Notes in Computer Science(), vol 9009. Springer, Cham. https://doi.org/10.1007/978-3-319-16631-5_19

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  • DOI: https://doi.org/10.1007/978-3-319-16631-5_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-16630-8

  • Online ISBN: 978-3-319-16631-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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