Constrained maximum likelihood learning of Bayesian Networks for facial action recognition

Cassio P. de Campos, Yan Tong, Qiang Ji

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

29 Citations (Scopus)


Probabilistic graphical models such as Bayesian Networks have been increasingly applied to many computer vision problems. Accuracy of inferences in such models depends on the quality of network parameters. Learning reliable parameters of Bayesian networks often requires a large amount of training data, which may be hard to acquire and may contain missing values. On the other hand, qualitative knowledge is available in many computer vision applications, and incorporating such knowledge can improve the accuracy of parameter learning. This paper describes a general framework based on convex optimization to incorporate constraints on parameters with training data to perform Bayesian network parameter estimation. For complete data, a global optimum solution to maximum likelihood estimation is obtained in polynomial time, while for incomplete data, a modified expectation-maximization method is proposed. This framework is applied to real image data from a facial action unit recognition problem and produces results that are similar to those of state-of-the-art methods.

Original languageEnglish
Title of host publicationComputer Vision - ECCV 2008
Subtitle of host publication10th European Conference on Computer Vision, Marseille, France, October 12-18, 2008, Proceedings, Part III
EditorsDavid Forsyth, Philip Torr, Andrew Zisserman
Place of PublicationBerlin
Number of pages14
ISBN (Electronic)978-3-540-88690-7
ISBN (Print)3540886893, 978-3-540-88689-1
Publication statusPublished - 10 Dec 2008
Externally publishedYes
Event10th European Conference on Computer Vision, ECCV 2008 - Marseille, France
Duration: 12 Oct 200818 Oct 2008

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 3
Volume5304 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference10th European Conference on Computer Vision, ECCV 2008

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