Joint Unsupervised Face Alignment and Behaviour Analysis

Lazaros Zafeiriou, Epameinondas Antonakos, Stefanos Zafeiriou, Maja Pantic

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

    6 Citations (Scopus)
    28 Downloads (Pure)


    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
    Number of pages17
    ISBN (Print)978-3-319-10592-5
    Publication statusPublished - Sep 2014
    Event13th European Conference on Computer Vision, ECCV 2014 - Zurich, Switzerland
    Duration: 6 Sep 201412 Sep 2014

    Publication series

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


    Conference13th European Conference on Computer Vision, ECCV 2014
    Other6-12 September 2014


    • 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


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