Coupled Gaussian Process Regression for pose-invariant facial expression recognition

Ognjen Rudovic, Ioannis Patras, Maja Pantic

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    38 Citations (Scopus)

    Abstract

    We present a novel framework for the recognition of facial expressions at arbitrary poses that is based on 2D geometric features. We address the problem by first mapping the 2D locations of landmark points of facial expressions in non-frontal poses to the corresponding locations in the frontal pose. Then, recognition of the expressions is performed by using any state-of-the-art facial expression recognition method (in our case, multi-class SVM). To learn the mappings that achieve pose normalization, we use a novel Gaussian Process Regression (GPR) model which we name Coupled Gaussian Process Regression (CGPR) model. Instead of learning single GPR model for all target pairs of poses at once, or learning one GPR model per target pair of poses independently of other pairs of poses, we propose CGPR model, which also models the couplings between the GPR models learned independently per target pairs of poses. To the best of our knowledge, the proposed method is the first one satisfying all: (i) being face-shape-model-free, (ii) handling expressive faces in the range from −45◦ to +45◦ pan rotation and from −30◦ to +30◦ tilt rotation, and (iii) performing accurately for continuous head pose despite the fact that the training was conducted only on a set of discrete poses.
    Original languageUndefined
    Title of host publicationProceedings of the 11th European Conference on Computer Vision, ECCV 2010
    EditorsKostas Daniilidis, Petros Maragos, Nikos Paragios
    Place of PublicationBerlin
    PublisherSpringer
    Pages350-363
    Number of pages14
    ISBN (Print)978-3-642-15551-2
    DOIs
    Publication statusPublished - 7 Sep 2010
    Event11th European Conference on Computer Vision, ECCV 2010 - Heraklion, Crete, Greece
    Duration: 5 Sep 201011 Sep 2010

    Publication series

    NameLecture Notes in Computer Science
    PublisherSpringer Verlag
    Volume6312

    Conference

    Conference11th European Conference on Computer Vision, ECCV 2010
    Period5/09/1011/09/10
    Other5-11 September 2010

    Keywords

    • IR-75893
    • METIS-276342
    • HMI-MI: MULTIMODAL INTERACTIONS
    • EWI-19491
    • EC Grant Agreement nr.: FP7/211486

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