Boosted regression active shape models

David Cristinacce, Tim Cootes

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


    We present an efficient method of fitting a set of local feature models to an image within the popular Active Shape Model (ASM) framework [3]. We compare two different types of non-linear boosted feature models trained using GentleBoost [9]. The first type is a conventional feature detector classifier, which learns a discrimination function between the appearance of a feature and the local neighbourhood. The second local model type is a boosted regression predictor which learns the relationship between the local neighbourhood appearance and the displacement from the true feature location. At run-time the second regression model is much more efficient as only the current feature patch needs to be processed. We show that within the local iterative search of the ASM the local feature regression provides improved localisation on two publicly available human face test sets as well as increasing the search speed by a factor of eight.
    Original languageEnglish
    Title of host publicationBMVC 2007 - Proceedings of the British Machine Vision Conference 2007|BMVC - Proc. Br. Mach. Vis. Conf.
    PublisherBMVA Press
    Publication statusPublished - 2007
    Event2007 18th British Machine Vision Conference, BMVC 2007 - Warwick
    Duration: 1 Jul 2007 → …


    Conference2007 18th British Machine Vision Conference, BMVC 2007
    Period1/07/07 → …


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