Facial expression recognition based on shape and texture

Xudong Xie, Kin Man Lam

Research output: Journal article publicationJournal articleAcademic researchpeer-review

61 Citations (Scopus)


In this paper, an efficient method for human facial expression recognition is presented. We first propose a representation model for facial expressions, namely the spatially maximum occurrence model (SMOM), which is based on the statistical characteristics of training facial images and has a powerful representation capability. Then the elastic shape-texture matching (ESTM) algorithm is used to measure the similarity between images based on the shape and texture information. By combining SMOM and ESTM, the algorithm, namely SMOM-ESTM, can achieve a higher recognition performance level. The recognition rates of the SMOM-ESTM algorithm based on the AR database and the Yale database are 94.5% and 94.7%, respectively.
Original languageEnglish
Pages (from-to)1003-1011
Number of pages9
JournalPattern Recognition
Issue number5
Publication statusPublished - 1 May 2009


  • Elastic shape-texture matching
  • Face recognition
  • Facial expression recognition
  • Gabor wavelets
  • Spatially maximum occurrence model

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Signal Processing


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