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Manifold Interpolation for an Efficient Hand Shape Recognition in the Irish Sign Language

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Advances in Visual Computing (ISVC 2016)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10073))

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Abstract

This paper presents interpolation using two-stage PCA for hand shape recognition. In the first stage PCA is performed on the entire training dataset of real human hand images. In the second stage, on separate sub-sets of the projected points in the first-stage eigenspace. The training set contains only a few pose angles. The output is a set of new interpolated manifolds, representing the missing data. The goal of this approach is to create a more robust dataset, able to recognise a hand image from an unknown rotation. We show some accuracy values in recognising unknown hand shapes.

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References

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Acknowledgements

This research was funded by CAPES/Science without Borders - Brazilian Program - Process: 9064-13-3.

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Authors and Affiliations

  1. School of Computing, Dublin City University, Dublin 9, Ireland

    Marlon Oliveira & Alistair Sutherland

  2. College of Computing and Information Technology, Arab Academy for Science and Technology, Alexandria, Egypt

    Mohamed Farouk

Authors
  1. Marlon Oliveira
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  2. Alistair Sutherland
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  3. Mohamed Farouk
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Corresponding author

Correspondence to Marlon Oliveira .

Editor information

Editors and Affiliations

  1. University of Nevada, Reno, Nevada, USA

    George Bebis

  2. NASA Ames Research Center, Moffett Field, California, USA

    Richard Boyle

  3. Lawrence Berkeley National Laboratory, Berkeley, California, USA

    Bahram Parvin

  4. Desert Research Institute, Reno, Nevada, USA

    Darko Koracin

  5. The Australian National University, O’Malley, Aust Capital Terr, Australia

    Fatih Porikli

  6. Pilot AI Labs, Redwood City, California, USA

    Sandra Skaff

  7. University of Florida, Gainesville, Florida, USA

    Alireza Entezari

  8. Google Inc., Mountain View, California, USA

    Jianyuan Min

  9. Osaka University, Osaka, Japan

    Daisuke Iwai

  10. The MOVES Institute, Monterey, California, USA

    Amela Sadagic

  11. University of Arizona, Tucson, Arizona, USA

    Carlos Scheidegger

  12. Université Paris-Sud, Orsay, France

    Tobias Isenberg

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Oliveira, M., Sutherland, A., Farouk, M. (2016). Manifold Interpolation for an Efficient Hand Shape Recognition in the Irish Sign Language. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2016. Lecture Notes in Computer Science(), vol 10073. Springer, Cham. https://doi.org/10.1007/978-3-319-50832-0_31

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  • DOI: https://doi.org/10.1007/978-3-319-50832-0_31

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-50831-3

  • Online ISBN: 978-3-319-50832-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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