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Robust Facial Feature Detection and Tracking for Head Pose Estimation in a Novel Multimodal Interface for Social Skills Learning

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Advances in Visual Computing (ISVC 2009)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 5876))

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Abstract

A robust and efficient facial feature detection and tracking approach for head pose estimation is presented in this paper. Six facial feature points (inner eye corners, nostrils and mouth corners) are detected and tracked using multiple cues including facial feature intensity and its probability distribution based on a novel histogram entropy analysis, geometric characteristics and motion information. The head pose is estimated from tracked points and a 3D facial feature model using POSIT and RANSAC algorithms. The proposed method demonstrates its capability in gaze tracking in a new multimodal technology enhanced learning (TEL) environment supporting learning of social communication skills.

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Author information

Authors and Affiliations

  1. School of Informatics, University of Edinburgh, UK

    Jingying Chen & Oliver Lemon

  2. Engineering and Research Centre for Information Technology on Education, Huazhong Normal University, Wuhan, P.R. China

    Jingying Chen

Authors
  1. Jingying Chen
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  2. Oliver Lemon
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, University of Nevada, Reno, USA

    George Bebis

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

    Richard Boyle

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

    Bahram Parvin

  4. Desert Research Institute, Reno, NV, USA

    Darko Koracin

  5. Graduate School of Science and Engineering, Saitama University, 255 Shimo-Okubo, Sakura-ku, Saitama-shi, 338-8570, 338-8570, Japan

    Yoshinori Kuno

  6. Microsoft Research, Redmond, WA, USA

    Junxian Wang

  7. Univ. of Zurich, Department of Informatics, Winterthurerstr. 190, P.O. Box, 8057, Zurich, Switzerland

    Renato Pajarola

  8. Lawrence Livermore National Laboratory, 94550, Livermore, CA, USA

    Peter Lindstrom

  9. University of Applied Sciences Bonn-Rhein-Sieg, 53754, Sankt Augustin, Germany

    André Hinkenjann

  10.  ,  

    Miguel L. Encarnação

  11. SCI Institute & School of Computing, University of Utah, 84112, Salt Lake City, UT, USA

    Cláudio T. Silva

  12. Desert Research Institute, 89512, Reno, NV, USA

    Daniel Coming

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© 2009 Springer-Verlag Berlin Heidelberg

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Chen, J., Lemon, O. (2009). Robust Facial Feature Detection and Tracking for Head Pose Estimation in a Novel Multimodal Interface for Social Skills Learning. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2009. Lecture Notes in Computer Science, vol 5876. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10520-3_56

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  • DOI: https://doi.org/10.1007/978-3-642-10520-3_56

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-10519-7

  • Online ISBN: 978-3-642-10520-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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