RAPS: Robust and Efficient Automatic Construction of Person-Specific Deformable Models

Christos Sagonas, Yannis Panagakis, Stefanos Zafeiriou, Maja Pantic

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

    36 Citations (Scopus)
    59 Downloads (Pure)


    The construction of Facial Deformable Models (FDMs) is a very challenging computer vision problem, since the face is a highly deformable object and its appearance drastically changes under different poses, expressions, and illuminations. Although several methods for generic FDMs construction, have been proposed for facial landmark localization in still images, they are insufficient for tasks such as facial behaviour analysis and facial motion capture where perfect landmark localization is required. In this case, person-specific FDMs (PSMs) are mainly employed, requiring manual facial landmark annotation for each person and person-specific training. In this paper, a novel method for the automatic construction of PSMs is proposed. To this end, an orthonormal subspace which is suitable for facial image reconstruction is learnt. Next, to correct the fittings of a generic model, image congealing (i.e., batch image aliment) is performed by employing only the learnt orthonormal subspace. Finally, the corrected fittings are used to construct the PSM. The image congealing problem is solved by formulating a suitable sparsity regularized rank minimization problem. The proposed method outperforms the state-of-the art methods that is compared to, in terms of both landmark localization accuracy and computational time.
    Original languageUndefined
    Title of host publicationProceedings of IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014
    Place of PublicationUSA
    Number of pages8
    ISBN (Print)978-1-4799-5117-8
    Publication statusPublished - Jun 2014
    Event27th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014 - Columbus, OH, USA, Columbus, United States
    Duration: 23 Jun 201428 Jun 2014
    Conference number: 27

    Publication series

    PublisherIEEE Computer Society
    ISSN (Print)1063-6919


    Conference27th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014
    Abbreviated titleCVPR 2014
    Country/TerritoryUnited States
    Other23-28 June 2014


    • HMI-HF: Human Factors
    • EWI-25816
    • METIS-309942
    • EC Grant Agreement nr.: FP7/288235
    • IR-95223
    • EC Grant Agreement nr.: FP7/2007-2013

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