Generic active appearance models revisited

Georgios Tzimiropoulos, Joan Alabort-i-Medina, Stefanos Zafeiriou, Maja Pantic

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

    47 Citations (Scopus)


    The proposed Active Orientation Models (AOMs) are gen- erative models of facial shape and appearance. Their main dierences with the well-known paradigm of Active Appearance Models (AAMs) are (i) they use a dierent statistical model of appearance, (ii) they are accompanied by a robust algorithm for model tting and parameter es- timation and (iii) and, most importantly, they generalize well to unseen faces and variations. Their main similarity is computational complex- ity. The project-out version of AOMs is as computationally ecient as the standard project-out inverse compositional algorithm which is ad- mittedly the fastest algorithm for tting AAMs. We show that not only does the AOM generalize well to unseen identities, but also it outper- forms state-of-the-art algorithms for the same task by a large margin. Finally, we prove our claims by providing Matlab code for reproducing our experiments.
    Original languageUndefined
    Title of host publicationProceedings of the 11th Asian Conference on Computer Vision, ACCV 2012
    Place of PublicationBerlin, Germany
    Number of pages14
    ISBN (Print)978-3-642-37430-2
    Publication statusPublished - 5 Nov 2012
    Event11th Asian Conference on Computer Vision, ACCV 2012 - Daejeon, Korea, Republic of
    Duration: 5 Nov 20129 Nov 2012
    Conference number: 11

    Publication series

    NameLecture Notes in Computer Science
    PublisherSpringer Verlag
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349


    Conference11th Asian Conference on Computer Vision, ACCV 2012
    Abbreviated titleACCV
    Country/TerritoryKorea, Republic of


    • EC Grant Agreement nr.: ERC-2007-STG-203143 (MAHNOB)
    • EC Grant Agreement nr.: FP7/288235
    • IR-84322
    • EWI-22885
    • METIS-296217
    • EC Grant Agreement nr.: FP7/2007-2013

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