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Real-Time Face Pose Estimation in Challenging Environments

  • Conference paper
Book cover Advanced Concepts for Intelligent Vision Systems (ACIVS 2013)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 8192))

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Abstract

A novel low-computation discriminative feature representation is introduced for face pose estimation in video context. The contributions of this work lie in the proposition of new approach which supports automatic face pose estimation with no need to manual initialization, able to handle different challenging problems without affecting the computational complexity ( 58 milliseconds per frame). We have applied Local Binary Patterns Histogram Sequence (LBPHS) on Gaussian and Gabor feature pictures to encode salient micro-patterns of multi-view face pose. Relying on LBPHS face representation, an SVM classifier was used to estimate face pose. Two series of experiments were performed to prove that our proposed approach, being simple and highly automated, can accurately and effectively estimate face pose. Additionally, experiments on face images with diverse resolutions prove that LBPHS features are efficient to low-resolution images, which is critical challenge in real-world applications where only low-resolution frames are available.

The original version of this chapter was revised: The copyright line was incorrect. This has been corrected. The Erratum to this chapter is available at DOI: 10.1007/978-3-319-02895-8_64

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Author information

Authors and Affiliations

  1. MIRACL-FSEG, University of Sfax, 3018, Sfax, Tunisia

    Mliki Hazar & Ben-Abdallah Hanêne

  2. MIRACL-FS, University of Sfax, 3018, Sfax, Tunisia

    Hammami Mohamed

Authors
  1. Mliki Hazar
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  2. Hammami Mohamed
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  3. Ben-Abdallah Hanêne
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Editor information

Editors and Affiliations

  1. DGA, 7-9 rue des mathurins, 92 221, Bagneux, France

    Jacques Blanc-Talon

  2. Institute of Control and Information Engineering, Poznań University of Technology, Piotrowo 3A, 60-965, Poznań, Poland

    Andrzej Kasinski

  3. Telecommunications and Information Processing (TELIN), Ghent University, St.-Pietersnieuwstraat 41, 9000, Ghent, Belgium

    Wilfried Philips

  4. CSIRO ICT Centre, Epping, Po Box 76, 1710, Sydney, NSW, Australia

    Dan Popescu

  5. University of Antwerp, Universiteitsplein 1, Building N., 2610, Wilrijk, Antwerp, Belgium

    Paul Scheunders

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© 2013 Springer-Verlag Berlin Heidelberg

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Hazar, M., Mohamed, H., Hanêne, BA. (2013). Real-Time Face Pose Estimation in Challenging Environments. In: Blanc-Talon, J., Kasinski, A., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2013. Lecture Notes in Computer Science, vol 8192. Springer, Cham. https://doi.org/10.1007/978-3-319-02895-8_11

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  • DOI: https://doi.org/10.1007/978-3-319-02895-8_11

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-02894-1

  • Online ISBN: 978-3-319-02895-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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