Optimization problems for fast AAM fitting in-the-wild

Georgios Tzimiropoulos, Maja Pantic

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

    172 Citations (Scopus)
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    We describe a very simple framework for deriving the most-well known optimization problems in Active Appearance Models (AAMs), and most importantly for providing efficient solutions. Our formulation results in two optimization problems for fast and exact AAM fitting, and one new algorithm which has the important advantage of being applicable to 3D. We show that the dominant cost for both forward and inverse algorithms is a few times mN which is the cost of projecting an image onto the appearance subspace. This makes both algorithms not only computationally realizable but also very attractive speed-wise for most current systems. Because exact AAM fitting is no longer computationally prohibitive, we trained AAMs in-the-wild with the goal of investigating whether AAMs benefit from such a training process. Our results show that although we did not use sophisticated shape priors, robust features or robust norms for improving performance, AAMs perform notably well and in some cases comparably with current state-of-the-art methods. We provide Matlab source code for training, fitting and reproducing the results presented in this paper at http://ibug.doc.ic.ac.uk/resources.
    Original languageUndefined
    Title of host publicationProceedings of the IEEE International Conference on Computer Vision, ICCV 2013
    Place of PublicationUSA
    PublisherIEEE Communications Society
    Number of pages8
    ISBN (Print)1550-5499
    Publication statusPublished - 3 Dec 2013
    EventIEEE International Conference on Computer Vision 2013 - Sydney Conference Centre, Sydney, Australia
    Duration: 1 Dec 20138 Dec 2013

    Publication series

    NameIEEE International Conference on Computer Vision
    PublisherIEEE Communications Society
    ISSN (Print)1550-5499


    ConferenceIEEE International Conference on Computer Vision 2013
    Abbreviated titleICCV 2013
    Internet address


    • HMI-HF: Human Factors
    • EWI-24238
    • METIS-302863
    • EC Grant Agreement nr.: FP7/2007-2013
    • IR-89696
    • EC Grant Agreement nr.: FP7/288235

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