Facial Expression Recognition Based on Dimension Model Using Sparse Coding

  • Young-suk Shin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3039)


We present an expression recognition system based on dimension model of internal states that is capable of identifying the various emotions using automated feature extraction. Feature vectors for facial expressions are extracted from a hybrid approach using fuzzy c-mean clustering algorithm and dynamic linking based on Gabor wavelet representation. The result of facial expression recognition is compared with dimensional values of internal states derived from semantic ratings of words related to emotion by experimental subjects. The dimensional model recognizes not only six facial expressions related to six basic emotions (happiness, sadness, surprise, angry, fear, disgust), but also expressions of various internal states. In this paper, with dimension model we have improved the limitation of expression recognition based on basic emotions, and have extracted features automatically with a new approach using FCM algorithm and the dynamic linking model.


Facial Expression Dimension Model Sparse Code Emotion Word Expression Recognition 
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 2004

Authors and Affiliations

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

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