Incremental Face Alignment in the Wild

Akshay Asthana, Ashish Asthana, Stefanos Zafeiriou, Shiyang Cheng, Maja Pantic

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

    321 Citations (Scopus)
    55 Downloads (Pure)


    The development of facial databases with an abundance of annotated facial data captured under unconstrained 'in-the-wild' conditions have made discriminative facial deformable models the de facto choice for generic facial landmark localization. Even though very good performance for the facial landmark localization has been shown by many recently proposed discriminative techniques, when it comes to the applications that require excellent accuracy, such as facial behaviour analysis and facial motion capture, the semi-automatic person-specific or even tedious manual tracking is still the preferred choice. One way to construct a person-specific model automatically is through incremental updating of the generic model. This paper deals with the problem of updating a discriminative facial deformable model, a problem that has not been thoroughly studied in the literature. In particular, we study for the first time, to the best of our knowledge, the strategies to update a discriminative model that is trained by a cascade of regressors. We propose very efficient strategies to update the model and we show that is possible to automatically construct robust discriminative person and imaging condition specific models 'in-the-wild' that outperform state-of-the-art generic face alignment strategies.
    Original languageUndefined
    Title of host publicationProceedings of IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014
    Place of PublicationUSA
    PublisherIEEE Computer Society
    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
    CountryUnited States
    Other23-28 June 2014


    • HMI-HF: Human Factors
    • METIS-309944
    • IR-95225
    • EWI-25818

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