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Facial Expression Recognition in Various Internal States Using Independent Component Analysis

  • Young-suk Shin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4069)

Abstract

This paper presents a new approach method to recognize facial expressions in various internal states using independent component analysis (ICA). We developed a representation of facial expression images based on independent component analysis for feature extraction of facial expressions. This representation consists of two steps. In the first step, we present a representation based on principal component analysis (PCA) excluded the first 2 principal components to reflect well the changes in facial expressions. Second, ICA representation from this PCA representation was developed. Finally, classification of facial expressions in various internal states was created on two dimensional structure of emotion with pleasure/displeasure dimension and arousal/sleep dimension. The proposed algorithm demonstrates the ability to discriminate the changes of facial expressions in various internal states. This system is possible to use in cognitive processes, social interaction and behavioral investigations of emotion.

Keywords

Facial Expression Internal State Independent Component Analysis Face Image Independent Component Analysis 
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 UniversityDong-gu, GwangjuKorea

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