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Expression Classification and Intensity Estimation by Expression Manifold Synthesis

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Intelligent Data Engineering and Automated Learning – IDEAL 2016 (IDEAL 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9937))

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Abstract

Facial expression and its dynamic property play an important role in interpreting and conveying emotions. Recently facial expression analysis has been an active topic in both psychology and computer vision. Most previous investigations have focused on the recognition of static images with intense expressions. Different from the previous work, we present an expression synthesis method for both expression classification and intensity estimation. By means of synthesising expression manifolds from neutral faces, the dynamic variations in facial expression can be modelled and analysed. Eigentransformation is utilised on both shape and expression details in generating novel expressions. Expression classification is performed on the expanded training sets with synthesised expression landmarks, and the intensity can be estimated with synthesised expression manifolds. Comprehensive experimental results conducted on the extended Cohn-Kanade database are reported.

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Author information

Authors and Affiliations

  1. School of Electrical and Electronic Engineering, The University of Manchester, Manchester, M13 9PL, UK

    Yao Peng & Hujun Yin

Authors
  1. Yao Peng
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  2. Hujun Yin
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Corresponding author

Correspondence to Yao Peng .

Editor information

Editors and Affiliations

  1. University of Manchester, Manchester, United Kingdom

    Hujun Yin

  2. Nanjing University, Nanjing, China

    Yang Gao

  3. Yangzhou University, Yangzhou, Jiangsu, China

    Bin Li

  4. Aeronautics and Astronautics, Nanjing University Aeronautics and Astronautics, Nanjing, China

    Daoqiang Zhang

  5. Nanjing Normal University, Nanjing, China

    Ming Yang

  6. Yangzhou University, Yangzhou, Jiangsu, China

    Yun Li

  7. Ostfalia University of Applied Sciences, Wolfenbüttel, Germany

    Frank Klawonn

  8. University of Seville, Seville, Spain

    Antonio J. Tallón-Ballesteros

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Peng, Y., Yin, H. (2016). Expression Classification and Intensity Estimation by Expression Manifold Synthesis. In: Yin, H., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2016. IDEAL 2016. Lecture Notes in Computer Science(), vol 9937. Springer, Cham. https://doi.org/10.1007/978-3-319-46257-8_68

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  • DOI: https://doi.org/10.1007/978-3-319-46257-8_68

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-46256-1

  • Online ISBN: 978-3-319-46257-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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