Recognizing Facial Expressions with PCA and ICA onto Dimension of the Emotion (original) (raw)

Abstract

This paper addresses the problem of facial expressions recognition using principal component analysis and independent component analysis onto dimension of the emotion. To reflect well the changes in facial expressions, a representation based on principal component analysis (PCA) excluded the first 2 principal components is presented, ICA representation from this PCA representation is developed. Facial expression performance in two dimensional structure was significant 90.9% in pleasure/displeasure dimension and 66.6% in the arousal/sleep dimension. The findings indicate that the two dimensional structure of emotion may reflect various emotion states as a stabled structure for the facial expression recognition.

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Authors and Affiliations

  1. Department of Information and telecommunication Engineering, Chosun University, #375 Seosuk-dong, Dong-gu, Gwangju, 501-759, Korea
    Young-suk Shin

Editor information

Editors and Affiliations

  1. Hong Kong University of Science and Technology,
    Dit-Yan Yeung
  2. Department of Computer Science, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China
    James T. Kwok
  3. Instituto de Telecomunicações, Instituto Superior Técnico, Lisbon, Portugal
    Ana Fred
  4. Department of Electrical and Electronic Engineering, University of Cagliari, Piazza d’Armi, 09123, Cagliari, Italy
    Fabio Roli
  5. Faculty of Electrical Engineering, Mathematics and Computer Science, Information and Communication Theory Group, Delft University of Technology, Delft, The Netherlands
    Dick de Ridder

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© 2006 Springer-Verlag Berlin Heidelberg

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Shin, Ys. (2006). Recognizing Facial Expressions with PCA and ICA onto Dimension of the Emotion. In: Yeung, DY., Kwok, J.T., Fred, A., Roli, F., de Ridder, D. (eds) Structural, Syntactic, and Statistical Pattern Recognition. SSPR /SPR 2006. Lecture Notes in Computer Science, vol 4109. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11815921\_101

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