Shared Gaussian Process Latent Variable Model for Multi-view Facial Expression Recognition

Stefanos Eleftheriadis, Ognjen Rudovic, Maja Pantic

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

    13 Citations (Scopus)
    15 Downloads (Pure)

    Abstract

    Facial-expression data often appear in multiple views either due to head-movements or the camera position. Existing methods for multi-view facial expression recognition perform classification of the target expressions either by using classifiers learned separately for each view or by using a single classifier learned for all views. However, these approaches do not explore the fact that multi-view facial expression data are different manifestations of the same facial-expression-related latent content. To this end, we propose a Shared Gaussian Process Latent Variable Model (SGPLVM) for classification of multi-view facial expression data. In this model, we first learn a discriminative manifold shared by multiple views of facial expressions, and then apply a (single) facial expression classifier, based on k-Nearest-Neighbours (kNN), to the shared manifold. In our experiments on the MultiPIE database, containing real images of facial expressions in multiple views, we show that the proposed model outperforms the state-of-the-art models for multi-view facial expression recognition.
    Original languageEnglish
    Title of host publicationAdvances in Visual Computing
    Subtitle of host publication9th International Symposium, ISVC 2013, Rethymnon, Crete, Greece, July 29-31, 2013. Proceedings
    EditorsGeorge Bebis, Richard Boyle, Bahram Parvin, Darko Koracin
    Place of PublicationBerlin Heidelberg
    PublisherSpringer
    Pages527-538
    Number of pages12
    VolumePart I
    ISBN (Print)978-3-642-41913-3
    DOIs
    Publication statusPublished - Jul 2013
    Event9th International Symposium on Visual Computing, ISVC 2013 - Rethymnon, Crete, Greece
    Duration: 29 Jul 201331 Jul 2013
    Conference number: 9

    Publication series

    NameLecture Notes in Computer Science
    PublisherSpringer
    Volume8033
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Conference

    Conference9th International Symposium on Visual Computing, ISVC 2013
    Abbreviated titleISVC
    CountryGreece
    CityRethymnon, Crete
    Period29/07/1331/07/13

    Keywords

    • EWI-24327
    • METIS-302654
    • IR-89368
    • HMI-HF: Human Factors

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  • Cite this

    Eleftheriadis, S., Rudovic, O., & Pantic, M. (2013). Shared Gaussian Process Latent Variable Model for Multi-view Facial Expression Recognition. In G. Bebis, R. Boyle, B. Parvin, & D. Koracin (Eds.), Advances in Visual Computing: 9th International Symposium, ISVC 2013, Rethymnon, Crete, Greece, July 29-31, 2013. Proceedings (Vol. Part I, pp. 527-538). (Lecture Notes in Computer Science; Vol. 8033). Berlin Heidelberg: Springer. https://doi.org/10.1007/978-3-642-41914-0_52