Facial Point Detection using Boosted Regression and Graph Models

Michel Valstar, Brais Martinez, Xavier Binefa, Maja Pantic

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

    280 Citations (Scopus)
    217 Downloads (Pure)


    Finding fiducial facial points in any frame of a video showing rich naturalistic facial behaviour is an unsolved problem. Yet this is a crucial step for geometric-featurebased facial expression analysis, and methods that use appearance-based features extracted at fiducial facial point locations. In this paper we present a method based on a combination of Support Vector Regression and Markov Random Fields to drastically reduce the time needed to search for a point’s location and increase the accuracy and robustness of the algorithm. Using Markov Random Fields allows us to constrain the search space by exploiting the constellations that facial points can form. The regressors on the other hand learn a mapping between the appearance of the area surrounding a point and the positions of these points, which makes detection of the points very fast and can make the algorithm robust to variations of appearance due to facial expression and moderate changes in head pose. The proposed point detection algorithm was tested on 1855 images, the results of which showed we outperform current state of the art point detectors.
    Original languageUndefined
    Title of host publicationIEEE Conference on Computer Vision and Pattern Recognition, CVPR 2010
    Place of PublicationUSA
    Number of pages8
    ISBN (Print)978-1-4244-6984-0
    Publication statusPublished - 17 Jun 2010
    Event23rd IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2010 - San Francisco, United States
    Duration: 13 Jun 201018 Jun 2010
    Conference number: 23

    Publication series

    PublisherIEEE Computer Society
    ISSN (Print)1063-6919


    Workshop23rd IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2010
    Abbreviated titleCVPR 2010
    Country/TerritoryUnited States
    CitySan Francisco


    • METIS-275925
    • IR-75978
    • EC Grant Agreement nr.: FP7/211486
    • EWI-19564
    • EC Grant Agreement nr.: FP7/231287

    Cite this