Semi-supervised learning for facial expression recognition

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

Resultado de la investigación: Contribución a una conferenciaArtículo de conferencia

19 Citas (Scopus)

Resumen

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.
Idioma originalInglés estadounidense
Páginas17-22
Número de páginas6
DOI
EstadoPublicada - 7 nov 2003
Publicado de forma externa
EventoProceedings of the 5th ACM SIGMM International Workshop on Multimedia Information Retrieval, MIR 2003 -
Duración: 7 nov 2003 → …

Conferencia

ConferenciaProceedings of the 5th ACM SIGMM International Workshop on Multimedia Information Retrieval, MIR 2003
Período7/11/03 → …

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  • Citar esto

    Cohen, I., Sebe, N., Cozman, F. G., & Huang, T. S. (2003). Semi-supervised learning for facial expression recognition. 17-22. Papel presentado en Proceedings of the 5th ACM SIGMM International Workshop on Multimedia Information Retrieval, MIR 2003, . https://doi.org/10.1145/973264.973268