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Hand Pose Regression via a Classification-Guided Approach

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Computer Vision – ACCV 2016 (ACCV 2016)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10113))

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Abstract

Hand pose estimation from single depth image has achieved great progress in recent years, however, up-to-data methods are still not satisfying the application requirements like in human-computer interaction. One possible reason is that existing methods try to learn a general regression function for all types of hand depth images. To handle this problem, we propose a novel “divide-and-conquer” method, which includes a classification step and a regression step. At first, a convolutional neural network classifier is used to classify the input hand depth image into different types. Then, an effective and efficient multiway cascaded random forest regressor is used to estimate the hand joints’ 3D positions. Experiments demonstrate that the proposed method achieves state-of-the-art performance on challenging dataset. Moreover, the proposed method can be easily combined with other regression method.

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Acknowledgement

We thank Zishun Liu for his helpful suggestions on this paper. This work was supported by the National Key R&D Program of China (No. 2016YFC0800501), NSF of China (Nos. 61672481, 61303148), NSF of Anhui Province, China (No. 1408085QF119), Specialized Research Fund for the Doctoral Program of Higher Education under contract (No. 20133402120002).

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Authors and Affiliations

  1. University of Science and Technology of China, Hefei, Anhui, China

    Hongwei Yang & Juyong Zhang

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  1. Hongwei Yang
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  2. Juyong Zhang
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Correspondence to Juyong Zhang .

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Editors and Affiliations

  1. National Tsing Hua University, Hsinchu, Taiwan

    Shang-Hong Lai

  2. Graz University of Technology, Graz, Austria

    Vincent Lepetit

  3. Drexel University, Philadelphia, Pennsylvania, USA

    Ko Nishino

  4. The University of Tokyo , Tokyo, Japan

    Yoichi Sato

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Yang, H., Zhang, J. (2017). Hand Pose Regression via a Classification-Guided Approach. In: Lai, SH., Lepetit, V., Nishino, K., Sato, Y. (eds) Computer Vision – ACCV 2016. ACCV 2016. Lecture Notes in Computer Science(), vol 10113. Springer, Cham. https://doi.org/10.1007/978-3-319-54187-7_30

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  • DOI: https://doi.org/10.1007/978-3-319-54187-7_30

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-54186-0

  • Online ISBN: 978-3-319-54187-7

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