Performance comparisons of facial expression recognition in JAFFE database

Frank Y. Shih, Chao Fa Chuang, Patrick S.P. Wang

Research output: Contribution to journalArticlepeer-review

132 Scopus citations


Facial expression provides an important behavioral measure for studies of emotion, cognitive processes, and social interaction. Facial expression recognition has recently become a promising research area. Its applications include human-computer interfaces, human emotion analysis, and medical care and cure. In this paper, we investigate various feature representation and expression classification schemes to recognize seven different facial expressions, such as happy, neutral, angry, disgust, sad, fear and surprise, in the JAFFE database. Experimental results show that the method of combining 2D-LDA (Linear Discriminant Analysis) and SVM (Support Vector Machine) outperforms others. The recognition rate of this method is 95.71% by using leave-one-out strategy and 94.13% by using cross-validation strategy. It takes only 0.0357 second to process one image of size 256 × 256.

Original languageEnglish (US)
Pages (from-to)445-459
Number of pages15
JournalInternational Journal of Pattern Recognition and Artificial Intelligence
Issue number3
StatePublished - May 2008

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence


  • Face recognition
  • Facial expression
  • Feature representation
  • Principal component analysis
  • Support vector machine


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