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Continuous Supervised Descent Method for Facial Landmark Localisation

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Computer Vision – ACCV 2016 (ACCV 2016)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10112))

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Abstract

Recent methods for facial landmark location perform well on close-to-frontal faces but have problems in generalising to large head rotations. In order to address this issue we propose a second order linear regression method that is both compact and robust against strong rotations. We provide a closed form solution, making the method fast to train. We test the method’s performance on two challenging datasets. The first has been intensely used by the community. The second has been specially generated from a well known 3D face dataset. It is considerably more challenging, including a high diversity of rotations and more samples than any other existing public dataset. The proposed method is compared against state-of-the-art approaches, including RCPR, CGPRT, LBF, CFSS, and GSDM. Results upon both datasets show that the proposed method offers state-of-the-art performance on near frontal view data, improves state-of-the-art methods on more challenging head rotation problems and keeps a compact model size.

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Notes

  1. 1.

    Code and data generation script available at https://github.com/moliusimon/csdm.

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Acknowledgement

The work of Marc Oliu is supported by the FI-DGR 2016 fellowship, granted by the Universities and Research Secretary of the Knowledge and Economy Department of the Generalitat de Catalunya. This work has been partially supported by the Spanish project TIN2013-43478-P, the European Comission Horizon 2020 granted project SEE.4C under call H2020-ICT-2015 and the U.S. National Institutes of Health under the grant MH096951.

Author information

Authors and Affiliations

  1. Universitat Oberta de Catalunya, 156 Rambla del Poblenou, Barcelona, Spain

    Marc Oliu

  2. Universitat de Barcelona, 585 Gran Via de les Corts Catalanes, Barcelona, Spain

    Ciprian Corneanu & Sergio Escalera

  3. Robotics Institute, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA

    László A. Jeni, Jeffrey F. Cohn & Takeo Kanade

  4. Computer Vision Center, O Building, UAB Campus, Bellaterra, Spain

    Marc Oliu, Ciprian Corneanu & Sergio Escalera

  5. Department of Psychology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA

    Jeffrey F. Cohn

Authors
  1. Marc Oliu
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  2. Ciprian Corneanu
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  3. László A. Jeni
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  4. Jeffrey F. Cohn
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  5. Takeo Kanade
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  6. Sergio Escalera
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Corresponding author

Correspondence to Marc Oliu .

Editor information

Editors and Affiliations

  1. National Tsing Hua University, Hsinchu, Taiwan

    Shang-Hong Lai

  2. Graz University of Technology, Graz, Austria

    Vincent Lepetit

  3. Drexel University, Philadelphia, Pennsylvania, USA

    Ko Nishino

  4. The University of Tokyo, Tokyo, Japan

    Yoichi Sato

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Oliu, M., Corneanu, C., Jeni, L.A., Cohn, J.F., Kanade, T., Escalera, S. (2017). Continuous Supervised Descent Method for Facial Landmark Localisation. In: Lai, SH., Lepetit, V., Nishino, K., Sato, Y. (eds) Computer Vision – ACCV 2016. ACCV 2016. Lecture Notes in Computer Science(), vol 10112. Springer, Cham. https://doi.org/10.1007/978-3-319-54184-6_8

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  • DOI: https://doi.org/10.1007/978-3-319-54184-6_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-54183-9

  • Online ISBN: 978-3-319-54184-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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