Robust and Efficient Parametric Face Alignment

Georgios Tzimiropoulos, Stefanos Zafeiriou, Maja Pantic

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    41 Citations (Scopus)
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    We propose a correlation-based approach to parametric object alignment particularly suitable for face analysis applications which require efficiency and robustness against occlusions and illumination changes. Our algorithm registers two images by iteratively maximizing their correlation coefficient using gradient ascent. We compute this correlation coefficient from complex gradients which capture the orientation of image structures rather than pixel intensities. The maximization of this gradient correlation coefficient results in an algorithm which is as computationally efficient as ℓ2 norm-based algorithms, can be extended within the inverse compositional framework (without the need for Hessian re-computation) and is robust to outliers. To the best of our knowledge, no other algorithm has been proposed so far having all three features. We show the robustness of our algorithm for the problem of face alignment in the presence of occlusions and non-uniform illumination changes. The code that reproduces the results of our paper can be found at
    Original languageUndefined
    Title of host publicationIEEE International Conference on Computer Vision (ICCV 2011)
    Place of PublicationUSA
    Number of pages8
    ISBN (Print)978-1-4577-1101-5
    Publication statusPublished - Nov 2011
    EventIEEE International Conference on Computer Vision 2011 - Fira de Barcelona, Barcelona, Spain
    Duration: 6 Nov 201113 Nov 2011

    Publication series

    PublisherIEEE Computer Society
    ISSN (Print)1550-5499


    ConferenceIEEE International Conference on Computer Vision 2011
    Abbreviated titleICCV 2011


    • METIS-285021
    • IR-79430
    • Face
    • Correlation
    • Cost function
    • Vectors
    • Robustness
    • Lighting
    • EWI-21315
    • EC Grant Agreement nr.: ERC/203143
    • Algorithm design and analysis

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