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On Constrained Local Model Feature Normalization for Facial Expression Recognition

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Intelligent Virtual Agents (IVA 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10011))

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Abstract

Real time user independent facial expression recognition is important for virtual agents but challenging. However, since in real time recognition users are not necessarily presenting all the emotions, some proposed methods are not applicable. In this paper, we present a new approach that instead of using the traditional base face normalization on whole face shapes, performs normalization on the point cloud of each landmark. The result shows that our method outperforms the other two when the user input does not contain all six universal emotions.

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Author information

Authors and Affiliations

  1. School of Computing and Information Sciences, Florida International University, 11200 SW 8th St, Miami, FL, 33199, USA

    Zhenglin Pan, Mihai Polceanu & Christine Lisetti

Authors
  1. Zhenglin Pan
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  2. Mihai Polceanu
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  3. Christine Lisetti
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Corresponding author

Correspondence to Zhenglin Pan .

Editor information

Editors and Affiliations

  1. University of Southern California, Los Angeles, California, USA

    David Traum

  2. University of Southern California, Los Angeles, California, USA

    William Swartout

  3. US Army Research Laboratory, Los Angeles, California, USA

    Peter Khooshabeh

  4. Universität Bielefeld, Bielefeld, Nordrhein-Westfalen, Germany

    Stefan Kopp

  5. University of Southern California, Los Angeles, California, USA

    Stefan Scherer

  6. University of Southern California, Los Angeles, California, USA

    Anton Leuski

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© 2016 Springer International Publishing AG

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Pan, Z., Polceanu, M., Lisetti, C. (2016). On Constrained Local Model Feature Normalization for Facial Expression Recognition. In: Traum, D., Swartout, W., Khooshabeh, P., Kopp, S., Scherer, S., Leuski, A. (eds) Intelligent Virtual Agents. IVA 2016. Lecture Notes in Computer Science(), vol 10011. Springer, Cham. https://doi.org/10.1007/978-3-319-47665-0_35

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  • DOI: https://doi.org/10.1007/978-3-319-47665-0_35

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-47664-3

  • Online ISBN: 978-3-319-47665-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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