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A Hand Data Model for Gesture Recognition

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Progress in Gestural Interaction

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Abstract

This paper describes a novel hand data model as a system evaluator that defines the minimum requirements for a robust recognition system. Interface devices for the user’s hand can be as simple as a switch to highly complex multi sensor gloves. A model is required that can cope with both diversity of the input devices and the rich variety of gesture based sign languages and user interfaces. We adopt an estimation of the Bayes error by calculating the classification error using distribution characteristics of the patterns, and this estimation is useful in classifying the gestures, evaluating the gesture input system, and specifying the gesture vocabulary.

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References

  1. A. Ashbrook. Geometric Histograms for Object Recognition: Developments and Analysis. PhD thesis, University of Sheffield (to be submitted ), 1996.

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Author information

Authors and Affiliations

  1. Department of Electronic and Electrical Engineering, University of Sheffield, Mappin Street, Sheffield, S1 3JD, UK

    S. Ben Salem & R. B. Yates

Authors
  1. S. Ben Salem
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  2. R. B. Yates
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Editor information

Editors and Affiliations

  1. Department of Computer Science, University of York, York, YO1 5DD, UK

    Philip A. Harling  & Alistair D. N. Edwards  & 

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© 1997 Springer-Verlag London

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Cite this paper

Salem, S.B., Yates, R.B. (1997). A Hand Data Model for Gesture Recognition. In: Harling, P.A., Edwards, A.D.N. (eds) Progress in Gestural Interaction. Springer, London. https://doi.org/10.1007/978-1-4471-0943-3_14

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  • DOI: https://doi.org/10.1007/978-1-4471-0943-3_14

  • Publisher Name: Springer, London

  • Print ISBN: 978-3-540-76094-8

  • Online ISBN: 978-1-4471-0943-3

  • eBook Packages: Springer Book Archive

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