Robust Statistical Face Frontalization

Christos Sagonas, Yannis Panagakis, Stefanos Zafeiriou, Maja Pantic

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

    70 Citations (Scopus)
    43 Downloads (Pure)


    Recently, it has been shown that excellent results can be achieved in both facial landmark localization and pose-invariant face recognition. These breakthroughs are attributed to the efforts of the community to manually annotate facial images in many different poses and to collect 3D facial data. In this paper, we propose a novel method for joint frontal view reconstruction and landmark localization using a small set of frontal images only. By observing that the frontal facial image is the one having the minimum rank of all different poses, an appropriate model which is able to jointly recover the frontalized version of the face as well as the facial landmarks is devised. To this end, a suitable optimization problem, involving the minimization of the nuclear norm and the matrix l1 norm is solved. The proposed method is assessed in frontal face reconstruction, face landmark localization, pose-invariant face recognition, and face verification in unconstrained conditions. The relevant experiments have been conducted on 8 databases. The experimental results demonstrate the effectiveness of the proposed method in comparison to the state-of-the-art methods for the target problems.
    Original languageEnglish
    Title of host publicationProceedings of The IEEE International Conference on Computer Vision (ICCV 2015)
    Place of PublicationUSA
    Number of pages9
    Publication statusPublished - Dec 2015
    EventIEEE International Conference on Computer Vision 2015 - Convention Center in Santiago, Santiago, Chile
    Duration: 7 Dec 201513 Dec 2015

    Publication series



    WorkshopIEEE International Conference on Computer Vision 2015
    Abbreviated titleICCV 2015
    Internet address


    • HMI-HF: Human Factors
    • METIS-316049
    • IR-99578
    • EWI-26840


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