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Expression Recognition Using Elastic Graph Matching

  • Conference paper
Affective Computing and Intelligent Interaction (ACII 2005)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3784))

Abstract

In this paper, we proposed a facial expression recognition method based on the elastic graph matching (EGM) approach.The EGM approach is widely considered very effective due to it’s robustness against face position and lighting variations. Among all the feature extraction methods which have been used with the EGM, we choose Gabor wavelet transform according to its good performance. In order to effectively represent the facial expression information, we choose the fiducial points from the local areas where the distortion caused by expression is obvious. The better performance of the proposed method is confirmed by the JAFFE facial expression database, compared to the some previous works. We can achieve the average expression recognition rate as high as 93.4%. Moreover, we can get face recognition result simultaneously in our experiment.

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Author information

Authors and Affiliations

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

    Yujia Cao, Wenming Zheng & Li Zhao

  2. Department of Radio Engineering, Southeast University, Nanjing, 210096, China

    Yujia Cao, Wenming Zheng, Li Zhao & Cairong Zhou

Authors
  1. Yujia Cao
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  2. Wenming Zheng
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  3. Li Zhao
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  4. Cairong Zhou
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Editor information

Editors and Affiliations

  1. National Laboratory of Pattern Recognition (NLPR), Institute of Automation, Chinese Academy of Sciences,  

    Jianhua Tao

  2. National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Tieniu Tan

  3. MIT Media Laboratory, 20 Ames Street, 02139, Cambridge, MA, USA

    Rosalind W. Picard

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© 2005 Springer-Verlag Berlin Heidelberg

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Cao, Y., Zheng, W., Zhao, L., Zhou, C. (2005). Expression Recognition Using Elastic Graph Matching. In: Tao, J., Tan, T., Picard, R.W. (eds) Affective Computing and Intelligent Interaction. ACII 2005. Lecture Notes in Computer Science, vol 3784. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11573548_2

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  • DOI: https://doi.org/10.1007/11573548_2

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-29621-8

  • Online ISBN: 978-3-540-32273-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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