Gaussian Process Domain Experts for Model Adaptation in Facial Behavior Analysis

Stefanos Eleftheriadis, Ognjen Rudovic, Marc P. Deisenroth, Maja Pantic

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

    10 Citations (Scopus)
    9 Downloads (Pure)

    Abstract

    We present a novel approach for supervised domain adaptation that is based upon the probabilistic framework of Gaussian processes (GPs). Specifically, we introduce domain-specific GPs as local experts for facial expression classification from face images. The adaptation of the classifier is facilitated in probabilistic fashion by conditioning the target expert on multiple source experts. Furthermore, in contrast to existing adaptation approaches, we also learn a target expert from available target data solely. Then, a single and confident classifier is obtained by combining the predictions from multiple experts based on their confidence. Learning of the model is efficient and requires no retraining/reweighting of the source classifiers. We evaluate the proposed approach on two publicly available datasets for multi-class (MultiPIE) and multi-label (DISFA) facial expression classification. To this end, we perform adaptation of two contextual factors: 'where' (view) and 'who' (subject). We show in our experiments that the proposed approach consistently outperforms both source and target classifiers, while using as few as 30 target examples. It also outperforms the state-of-the-art approaches for supervised domain adaptation.
    Original languageEnglish
    Title of host publication2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW 2016)
    Subtitle of host publicationLas Vegas, Nevada, USA, 26 June - 1 July 2016
    Place of PublicationPiscataway, NJ
    PublisherIEEE Computer Society
    Pages1469-1477
    Number of pages9
    ISBN (Electronic)978-1-5090-1438-5
    ISBN (Print)978-1-5090-1437-8
    DOIs
    Publication statusPublished - Jun 2016
    Event29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016 - Las Vegas, NV, USA, Las Vegas, United States
    Duration: 26 Jun 20161 Jul 2016
    Conference number: 29

    Publication series

    NameIEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
    PublisherIEEE Computer Society
    ISSN (Print)2160-7516

    Conference

    Conference29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016
    Abbreviated titleCVPR 2016
    CountryUnited States
    CityLas Vegas
    Period26/06/161/07/16
    Other29 June - 1 July 2016

    Keywords

    • HMI-HF: Human Factors
    • facial behavior analysis
    • IR-103096
    • Gaussian processes
    • METIS-320877
    • EWI-27133

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