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.


Principal Component Analysis Facial Expression Independent Component Analysis Independent Component Analysis Emotion Word 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Young-suk Shin
    • 1
  1. 1.Department of Information and telecommunication EngineeringChosun UniversityGwangjuKorea

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