Supervised super-vector encoding for facial expression recognition

Usman Tariq, Jianchao Yang, Thomas S. Huang

Research output: Contribution to journalArticlepeer-review

Abstract

Expression recognition from faces with varying pose and illumination conditions is a challenging research area with growing interest. In this paper, we develop a novel supervised super-vector encoding framework to learn discriminative image feature representations. The framework is then validated on the Multi-PIE and BU3D-FE databases for multi-view facial expression recognition. Extensive experiments show that our supervised framework gives significant improvement over the unsupervised counterpart and outperforms the state-of-the-arts.

Original languageEnglish (US)
Pages (from-to)89-95
Number of pages7
JournalPattern Recognition Letters
Volume46
DOIs
StatePublished - Sep 1 2014

Keywords

  • Face biometrics
  • Facial expression recognition
  • GMM learning

ASJC Scopus subject areas

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

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