Joint Unsupervised Face Alignment and Behaviour Analysis

Lazaros Zafeiriou, Epameinondas Antonakos, Stefanos Zafeiriou, Maja Pantic

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    6 Citations (Scopus)
    26 Downloads (Pure)

    Abstract

    The predominant strategy for facial expressions analysis and temporal analysis of facial events is the following: a generic facial landmarks tracker, usually trained on thousands of carefully annotated examples, is applied to track the landmark points, and then analysis is performed using mostly the shape and more rarely the facial texture. This paper challenges the above framework by showing that it is feasible to perform joint landmarks localization (i.e. spatial alignment) and temporal analysis of behavioural sequence with the use of a simple face detector and a simple shape model. To do so, we propose a new component analysis technique, which we call Autoregressive Component Analysis (ARCA), and we show how the parameters of a motion model can be jointly retrieved. The method does not require the use of any sophisticated landmark tracking methodology and simply employs pixel intensities for the texture representation.
    Original languageEnglish
    Title of host publicationProceedings of the 13th European Conference on Computer Vision, ECCV 2014
    Place of PublicationSwitzerland
    PublisherSpringer
    Pages167-183
    Number of pages17
    ISBN (Print)978-3-319-10592-5
    DOIs
    Publication statusPublished - Sep 2014

    Publication series

    NameLecture Notes in Computer Science
    PublisherSpringer International Publishing
    Volume8692
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

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    Keywords

    • HMI-HF: Human Factors
    • EC Grant Agreement nr.: FP7/288235
    • EWI-25814
    • time series alignment
    • METIS-310010
    • EC Grant Agreement nr.: FP7/2007-2013
    • Face alignment
    • Slow feature analysis
    • IR-95221
    • EC Grant Agreement nr.: FP7/611153

    Cite this

    Zafeiriou, L., Antonakos, E., Zafeiriou, S., & Pantic, M. (2014). Joint Unsupervised Face Alignment and Behaviour Analysis. In Proceedings of the 13th European Conference on Computer Vision, ECCV 2014 (pp. 167-183). (Lecture Notes in Computer Science; Vol. 8692). Switzerland: Springer. https://doi.org/10.1007/978-3-319-10593-2_12