Multi-output Laplacian Dynamic Ordinal Regression for Facial Expression Recognition and Intensity Estimation

Ognjen Rudovic, Vladimir Pavlovic, Maja Pantic

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

    80 Citations (Scopus)
    186 Downloads (Pure)


    Automated facial expression recognition has received increased attention over the past two decades. Existing works in the field usually do not encode either the temporal evolution or the intensity of the observed facial displays. They also fail to jointly model multidimensional (multi-class) continuous facial behaviour data; binary classifiers - one for each target basic-emotion class - are used instead. In this paper, intrinsic topology of multidimensional continuous facial affect data is first modeled by an ordinal manifold. This topology is then incorporated into the Hidden Conditional Ordinal Random Field (H-CORF) framework for dynamic ordinal regression by constraining H-CORF parameters to lie on the ordinal manifold. The resulting model attains simultaneous dynamic recognition and intensity estimation of facial expressions of multiple emotions. To the best of our knowledge, the proposed method is the first one to achieve this on both deliberate as well as spontaneous facial affect data.
    Original languageUndefined
    Title of host publicationProceedings of IEEE International Conference on Computer Vision and Pattern Recognition (CVPR 2012)
    Place of PublicationWashington DC
    Number of pages8
    ISBN (Print)978-1-4673-1226-4
    Publication statusPublished - 16 Jun 2012
    Event25th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2012 - Washington, United States
    Duration: 18 Jun 201220 Jun 2012
    Conference number: 25

    Publication series

    PublisherIEEE Computer Society
    ISSN (Print)1063-6919


    Conference25th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2012
    Abbreviated titleCVPR 2012
    Country/TerritoryUnited States


    • EWI-23054
    • METIS-296291
    • IR-84319

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