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Learning Fast Hand Pose Recognition

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Computer Vision and Machine Learning with RGB-D Sensors

Part of the book series: Advances in Computer Vision and Pattern Recognition ((ACVPR))

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Abstract

Practical real-time hand pose recognition requires a classifier of high accuracy, running in a few millisecond speed. We present a novel classifier architecture, the Discriminative Ferns Ensemble (DFE), for addressing this challenge. The classifier architecture optimizes both classification speed and accuracy when a large training set is available. Speed is obtained using simple binary features and direct indexing into a set of tables, and accuracy by using a large capacity model and careful discriminative optimization. The proposed framework is applied to the problem of hand pose recognition in depth and infrared images, using a very large training set. Both the accuracy and the classification time obtained are considerably superior to relevant competing methods, allowing one to reach accuracy targets with runtime orders of magnitude faster than the competition. We show empirically that using DFE, we can significantly reduce classification time by increasing training sample size for a fixed target accuracy. Finally, scalability to a large number of classes is tested using a synthetically generated data set of \(81\) classes.

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Notes

  1. 1.

    Assuming that the underlying space is of dimension higher than \(K\) and MK, respectively, which are satisfied for the image sizes considered.

  2. 2.

    Note that FN is measured at false-positive rate of 2 %. Hence, FN near 50 % is far better than random. At FP \(=\) 10 % the false-negative rates of Naive Bayes MI bits and Rand bits drops to 11 and 18 %, respectively.

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Author information

Authors and Affiliations

  1. Microsoft Research, Herzelia, Israel

    Eyal Krupka, Alon Vinnikov, Ben Klein, Aharon Bar-Hillel & Daniel Freedman

  2. Microsoft Console dev R&D, Redmond, WA, USA

    Simon Stachniak

  3. Microsoft Research Cambridge, Cambridge, UK

    Cem Keskin

Authors
  1. Eyal Krupka
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  2. Alon Vinnikov
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  3. Ben Klein
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  4. Aharon Bar-Hillel
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  5. Daniel Freedman
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  6. Simon Stachniak
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  7. Cem Keskin
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Corresponding author

Correspondence to Aharon Bar-Hillel .

Editor information

Editors and Affiliations

  1. University of Sheffield, United Kingdom

    Ling Shao

  2. Civolution Technology, Eindhoven, The Netherlands

    Jungong Han

  3. Microsoft Research, Cambridge, United Kingdom

    Pushmeet Kohli

  4. Microsoft Research, Redmond, Washington, USA

    Zhengyou Zhang

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© 2014 Springer International Publishing Switzerland

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Krupka, E. et al. (2014). Learning Fast Hand Pose Recognition. In: Shao, L., Han, J., Kohli, P., Zhang, Z. (eds) Computer Vision and Machine Learning with RGB-D Sensors. Advances in Computer Vision and Pattern Recognition. Springer, Cham. https://doi.org/10.1007/978-3-319-08651-4_13

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  • DOI: https://doi.org/10.1007/978-3-319-08651-4_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-08650-7

  • Online ISBN: 978-3-319-08651-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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