Human-computer interaction: A Bayesian network approach

Nicu Sebe, Ira Cohen, Thomas S. Huang, Theo Gevers

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

Abstract

Human-computer interaction (HCI) lies at the crossroads of many scientific areas including artificial intelligence, computer vision, face recognition, motion tracking, etc. In this paper, we discuss training probabilistic classifiers with labeled and unlabeled data for human-computer interaction applications. We provide an analysis which shows under what conditions unlabeled data can be used in learning to improve classificaion performance and we investigate the implications of this analysis to a specifie type of probabilistic classifiers, Bayesian networks. Finally, we show how the resulting algorithms are successfully employed in facial expression recognition, face detection, and skin detection.

Original languageEnglish (US)
Title of host publicationISSCS 2005
Subtitle of host publicationInternational Symposium on Signals, Circuits and Systems - Proceedings
Pages343-346
Number of pages4
DOIs
StatePublished - Dec 1 2005
EventISSCS 2005: International Symposium on Signals, Circuits and Systems - Iasi, Romania
Duration: Jul 14 2005Jul 15 2005

Publication series

NameISSCS 2005: International Symposium on Signals, Circuits and Systems - Proceedings
Volume1

Other

OtherISSCS 2005: International Symposium on Signals, Circuits and Systems
Country/TerritoryRomania
CityIasi
Period7/14/057/15/05

ASJC Scopus subject areas

  • Engineering(all)

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