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Facial Expression Recognition Using Kernel Discriminant Plane

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Advances in Neural Networks – ISNN 2004 (ISNN 2004)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3173))

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Abstract

Facial expression recognition (FER) based on a new nonlinear feature extraction method, called kernel discriminant plane (KDP), is proposed in this paper. KDP is a nonlinear extension of the Sammon’s optimal discriminant plane (ODP) via the kernel trick. The recognition procedure is divided into two steps: (1) we select 34 fiducial points manually from each facial image and use the coordinates of these points as the input data of the facial image; (2) we construct a multiple binary classifier for the classification purpose of this task. The better performance of the proposed method is confirmed by the Japanese Female Facial Expression (JAFFE) database.

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Author information

Authors and Affiliations

  1. The Research Center for Science of Learning, Southeast University, Nanjing, Jiangsu, 210096, China

    Wenming Zheng, Xiaoyan Zhou & Li Zhao

  2. The Engineering Research Center of Information Processing and Application, Southeast University, Nanjing, Jiangsu, 210096, China

    Wenming Zheng, Xiaoyan Zhou, Cairong Zou & Li Zhao

Authors
  1. Wenming Zheng
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  2. Xiaoyan Zhou
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  3. Cairong Zou
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  4. Li Zhao
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Editor information

Editors and Affiliations

  1. School of Electronic and Information Engineering, Dalian University of Technology, 116023, Dalian, China

    Fu-Liang Yin

  2. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China

    Jun Wang

  3. School of Electronic and Information Engineering, Dalian University of Technology, 116023, Dalian, Liaoning, China

    Chengan Guo

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© 2004 Springer-Verlag Berlin Heidelberg

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Zheng, W., Zhou, X., Zou, C., Zhao, L. (2004). Facial Expression Recognition Using Kernel Discriminant Plane. In: Yin, FL., Wang, J., Guo, C. (eds) Advances in Neural Networks – ISNN 2004. ISNN 2004. Lecture Notes in Computer Science, vol 3173. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28647-9_156

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  • DOI: https://doi.org/10.1007/978-3-540-28647-9_156

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-22841-7

  • Online ISBN: 978-3-540-28647-9

  • eBook Packages: Springer Book Archive

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