Semi-supervised learning for facial expression recognition

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

Research output: Contribution to conferenceConference Paper

21 Scopus citations

Abstract

Copyright 2003 ACM. 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 languageAmerican English
Pages17-22
Number of pages6
DOIs
StatePublished - 7 Nov 2003
Externally publishedYes
EventProceedings of the 5th ACM SIGMM International Workshop on Multimedia Information Retrieval, MIR 2003 -
Duration: 7 Nov 2003 → …

Conference

ConferenceProceedings of the 5th ACM SIGMM International Workshop on Multimedia Information Retrieval, MIR 2003
Period7/11/03 → …

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