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IAPR Workshop on Artificial Neural Networks in Pattern Recognition

ANNPR 2012: Artificial Neural Networks in Pattern Recognition pp 139–150Cite as

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Facial Expression Recognition Using Game Theory

Facial Expression Recognition Using Game Theory

  • Kaushik Roy22 &
  • Mohamed S. Kamel22 
  • Conference paper
  • 1416 Accesses

  • 3 Citations

Part of the Lecture Notes in Computer Science book series (LNAI,volume 7477)

Abstract

Accurate detection of lip contour is important in many application areas, including biometric authentication, human computer interaction, and facial expression recognition. In this paper, we propose a new lip boundary localization scheme based on Game Theory (GT) to improve the facial expression detection performance. In addition, we use GT for selecting the proper set of facial features. We apply the Extended Contribution-Selection Algorithm (ECSA) for the dimensionality reduction of the facial features using a coalitional GT-based framework. We have conducted several sets of experiments to evaluate the proposed approach. The results show that the proposed approach has achieved recognition rates of 93.1% and 92.7% on the JAFFE and CK+ datasets, respectively.

Keywords

  • Facial expression recognition
  • coalitional game theory
  • extended contribution selection algorithm

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References

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Author information

Authors and Affiliations

  1. Centre for Pattern Analysis and Machine Intelligence, University of Waterloo, ON, Canada

    Kaushik Roy & Mohamed S. Kamel

Authors
  1. Kaushik Roy
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  2. Mohamed S. Kamel
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Editor information

Editors and Affiliations

  1. Fondazione Bruno Kessler (FBK), 38123, Trento, Italy

    Nadia Mana

  2. Institute of Neural Information Processing, University of Ulm, 89069, Ulm, Germany

    Friedhelm Schwenker

  3. Dipartimento di Ingegneria dell’Informazione, Università di Siena, 53100, Siena, Italy

    Edmondo Trentin

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© 2012 Springer-Verlag Berlin Heidelberg

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Cite this paper

Roy, K., Kamel, M.S. (2012). Facial Expression Recognition Using Game Theory. In: Mana, N., Schwenker, F., Trentin, E. (eds) Artificial Neural Networks in Pattern Recognition. ANNPR 2012. Lecture Notes in Computer Science(), vol 7477. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33212-8_13

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  • DOI: https://doi.org/10.1007/978-3-642-33212-8_13

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  • Print ISBN: 978-3-642-33211-1

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