Feature detection and tracking with constrained local models

David Cristinacce, Tim Cootes

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


    We present an efficient and robust model matching method which uses a joint shape and texture appearance model to generate a set of region template detectors. The model is fitted to an unseen image in an iterative manner by generating templates using the joint model and the current parameter estimates, correlating the templates with the target image to generate response images and optimising the shape parameters so as to maximise the sum of responses. The appearance model is similar to that used in the AAM [1]. However in our approach the appearance model is used to generate likely feature templates, instead of trying to approximate the image pixels directly. We show that when applied to human faces, our Constrained Local Model (CLM) algorithm is more robust and more accurate than the original AAM search method, which relies on the image reconstruction error to update the model parameters. We demonstrate improved localisation accuracy on two publicly available face data sets and improved tracking on a challenging set of in-car face sequences.
    Original languageEnglish
    Title of host publicationBMVC 2006 - Proceedings of the British Machine Vision Conference 2006|BMVC - Proc. Br. Mach. Vis. Conf.
    Place of PublicationEdinburgh
    PublisherBMVA Press
    Number of pages9
    Publication statusPublished - 2006
    Event2006 17th British Machine Vision Conference, BMVC 2006 - Edinburgh
    Duration: 1 Jul 2006 → …


    Conference2006 17th British Machine Vision Conference, BMVC 2006
    Period1/07/06 → …


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