Semi-supervised learning for facial expression recognition

Ira Cohen, Nicu Sebe, Fabio G. Cozman, Thomas S. Huang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Automatic classification by machines is one of the basic tasks required in any pattern recognition and human computer interaction applications. In this paper, we discuss training probabilistic classifiers with labeled and unlabeled data. We provide an analysis which shows under what conditions unlabeled data can be used in learning to improve classification performance. We discuss the implications of this analysis to a specific type of probabilistic classifiers, Bayesian networks, and propose a structure learning algorithm that can utilize unlabeled data to improve classification. Finally, we show how the resulting algorithms are successfully employed in a facial expression recognition application.

Original languageEnglish (US)
Title of host publicationProceedings of the 5th ACM SIGMM International Workshop on Multimedia Information Retrieval, MIR 2003
PublisherAssociation for Computing Machinery, Inc
Pages17-22
Number of pages6
ISBN (Electronic)1581137788, 9781581137781
DOIs
StatePublished - Nov 7 2003
Event5th ACM SIGMM International Workshop on Multimedia Information Retrieval, MIR 2003 - Berkeley, United States
Duration: Nov 7 2003 → …

Publication series

NameProceedings of the 5th ACM SIGMM International Workshop on Multimedia Information Retrieval, MIR 2003

Other

Other5th ACM SIGMM International Workshop on Multimedia Information Retrieval, MIR 2003
Country/TerritoryUnited States
CityBerkeley
Period11/7/03 → …

Keywords

  • Bayesian networks
  • Facial expression recognition
  • Semi-supervised learning

ASJC Scopus subject areas

  • Media Technology
  • Software
  • Information Systems
  • Computer Science Applications
  • Signal Processing

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