Facial Behaviometrics: the Case of Facial Deformation in Spontenaous Smile/Laughter

Stefanos Zafeiriou, Maja Pantic

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

    9 Citations (Scopus)


    In this paper we explore the use of dense facial deformation in spontaneous smile/laughter as a biometric signature. The facial deformation is calculated between a neutral image (as neutral we define the least expressive image of the smile/laughter episode) and the apex of spontaneous smile/laughter (as apex we define the frame of the maximum facial change/deformation) and its complex representation is regarded. Subsequently, supervised and unsupervised complex dimensionality reduction techniques, namely the complex Principal Component Analysis (PCA) and the complex Linear Discriminant Analysis (LDA), are applied at the complex vector fields for feature extraction. We demonstrate the efficacy of facial deformation as a mean for person verification in a database of spontaneous smiles/laughters.
    Original languageUndefined
    Title of host publicationIEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPR-W 2011)
    Place of PublicationUSA
    PublisherIEEE Computer Society
    Number of pages7
    ISBN (Print)978-1-4577-0529-8
    Publication statusPublished - Jun 2011
    Event24th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2011 - Colorado Springs, United States
    Duration: 20 Jun 201125 Jun 2011
    Conference number: 24

    Publication series

    PublisherIEEE Computer Society


    Conference24th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2011
    Abbreviated titleCVPR 2011
    CountryUnited States
    CityColorado Springs


    • METIS-285027
    • IR-79433
    • Face Recognition
    • Databases
    • Humans
    • EWI-21325
    • Principal component analysis
    • Speech
    • Training
    • EC Grant Agreement nr.: ERC/203143
    • Vectors

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