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Teacher and Student Joint Learning for Compact Facial Landmark Detection Network

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MultiMedia Modeling (MMM 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10704))

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Abstract

Compact neural networks with limited memory and computation are demanding in recently popularized mobile applications. The reduction of network parameters is an important priority. In this paper, we address a compact neural network for facial landmark detection. The facial landmark detection is a frontal module that is mandatorily required for face analysis applications. We propose a new teacher and student joint learning method applicable to a compact facial landmark detection network. In the proposed learning scheme, the compact architecture of student regression network is learned jointly with the fully connected layer of the teacher regression network so that they are mimicked each other. To demonstrate the effectiveness of the proposed learning method, experiments were performed on a public database. The experimental results showed that the proposed method could reduce network parameters while maintaining comparable performance to state-of-the-art methods.

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Acknowledgements

This work was supported by Institute for Information and communications Technology Promotion (IITP) grant funded by the Korea government (MSIT) (No. 2017-0-00111, Practical technology development of high performing emotion recognition and facial expression based authentication using deep network).

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Authors and Affiliations

  1. Image and Video Systems Laboratory, School of Electrical Engineering, KAIST, Daejeon, South Korea

    Hong Joo Lee, Wissam J. Baddar, Hak Gu Kim, Seong Tae Kim & Yong Man Ro

Authors
  1. Hong Joo Lee
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  2. Wissam J. Baddar
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  3. Hak Gu Kim
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  4. Seong Tae Kim
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  5. Yong Man Ro
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Corresponding author

Correspondence to Yong Man Ro .

Editor information

Editors and Affiliations

  1. Alpen-Adria-Universität Klagenfurt, Klagenfurt, Austria

    Klaus Schoeffmann

  2. Chulalongkorn University, Bangkok, Thailand

    Thanarat H. Chalidabhongse

  3. City University of Hong Kong, Hong Kong, China

    Chong Wah Ngo

  4. Chulalongkorn University, Bangkok, Thailand

    Supavadee Aramvith

  5. Dublin City University, Dublin, Ireland

    Noel E. O’Connor

  6. Gwangju Institute of Science and Technology, Gwangju, Korea (Republic of)

    Yo-Sung Ho

  7. Tampere University of Technology, Tampere, Finland

    Moncef Gabbouj

  8. Rutgers University, Piscataway, New Jersey, USA

    Ahmed Elgammal

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Lee, H.J., Baddar, W.J., Kim, H.G., Kim, S.T., Ro, Y.M. (2018). Teacher and Student Joint Learning for Compact Facial Landmark Detection Network. In: Schoeffmann, K., et al. MultiMedia Modeling. MMM 2018. Lecture Notes in Computer Science(), vol 10704. Springer, Cham. https://doi.org/10.1007/978-3-319-73603-7_40

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  • DOI: https://doi.org/10.1007/978-3-319-73603-7_40

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-73602-0

  • Online ISBN: 978-3-319-73603-7

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