Multi-Conditional Latent Variable Model for Joint Facial Action Unit Detection

Stefanos Eleftheriadis, Ognjen Rudovic, Maja Pantic

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

    85 Citations (Scopus)
    29 Downloads (Pure)


    We propose a novel multi-conditional latent variable model for simultaneous facial feature fusion and detection of facial action units. In our approach we exploit the structure-discovery capabilities of generative models such as Gaussian processes, and the discriminative power of classifiers such as logistic function. This leads to superior performance compared to existing classifiers for the target task that exploit either the discriminative or generative property, but not both. The model learning is performed via an efficient, newly proposed Bayesian learning strategy based on Monte Carlo sampling. Consequently, the learned model is robust to data overfitting, regardless of the number of both input features and jointly estimated facial action units. Extensive qualitative and quantitative experimental evaluations are performed on three publicly available datasets (CK+, Shoulder-pain and DISFA). We show that the proposed model outperforms the state-of-the-art methods for the target task on (i) feature fusion, and (ii) multiple facial action unit detection.
    Original languageEnglish
    Title of host publicationProceedings of the IEEE International Conference on Computer Vision (ICCV 2015)
    Place of PublicationUSA
    Number of pages9
    Publication statusPublished - Dec 2015
    EventIEEE International Conference on Computer Vision 2015 - Convention Center in Santiago, Santiago, Chile
    Duration: 7 Dec 201513 Dec 2015

    Publication series



    WorkshopIEEE International Conference on Computer Vision 2015
    Abbreviated titleICCV 2015
    Internet address


    • HMI-HF: Human Factors
    • EC Grant Agreement nr.: FP7/611153
    • IR-99579
    • METIS-316050
    • EWI-26841


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