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Volume 24 Issue 4
July  2009
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Young-Suk Shin. Facial Expression Recognition of Various Internal States via Manifold Learning[J]. Journal of Computer Science and Technology, 2009, 24(4): 745-752.
Citation: Young-Suk Shin. Facial Expression Recognition of Various Internal States via Manifold Learning[J]. Journal of Computer Science and Technology, 2009, 24(4): 745-752.
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Facial Expression Recognition of Various Internal States via Manifold Learning

  • Department of Information and Communication Engineering, Chosun University, #375 Seosuk-dong, Dong-gu, Gwangju 501-759, Korea

Funds: This study was supported by research funds from Chosun University, 2008.
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  • Author Bio:

    Young-Suk Shin is an assistant professor in Department ofInformation and Communication Engineering, Chosun University. Shereceived the Ph.D. degree in computer science from Yonsei University,Seoul, in 2001. Her research interests include pattern recognition,biometrics, cognitive modeling, facial expression recognition, emotionrecognition, virtual reality, and human-computer-interaction. From 2008to 2009, she is visiting the Distributed & Collabo-rative VirtualEnvironments Research Laboratory at the University of Ottawa, Canada.

  • Received Date: October 21, 2008
  • Revised Date: April 16, 2009
  • Published Date: July 04, 2009
    Abstract
  • Emotions are becoming increasingly important in human-centered interaction architectures. Recognition of facial expressions, which are central to human-computer interactions, seems natural and desirable. However, facial expressions include mixed emotions, continuous rather than discrete, which vary from moment to moment. This paper represents a novel method of recognizing facial expressions of various internal states via manifold learning, to achieve the aim of human-centered interaction studies. A critical review of widely used emotion models is described, then, facial expression features of various internal states via the locally linear embedding (LLE) are extracted. The recognition of facial expressions is created with the pleasure-displeasure and arousal-sleep dimensions in a two-dimensional model of emotion. The recognition result of various internal state expressions that mapped to the embedding space via the LLE algorithm can effectively represent the structural nature of the two-dimensional model of emotion. Therefore our research has established that the relationship between facial expressions of various internal states can be elaborated in the two-dimensional model of emotion, via the locally linear embedding algorithm.
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    • References (30)
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