Output-associative RVM regression for dimensional and continuous emotion prediction

Mihalis A. Nicolaou, Hatice Gunes, Maja Pantic

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

    34 Citations (Scopus)

    Abstract

    Many problems in machine learning and computer vision consist of predicting multi-dimensional output vectors given a specific set of input features. In many of these problems, there exist inherent temporal and spacial dependencies between the output vectors, as well as repeating output patterns and input-output associations, that can provide more robust and accurate predictors when modelled properly. With this intrinsic motivation, we propose a novel Output-Associative Relevance Vector Machine (OA-RVM) regression framework that augments the traditional RVM regression by being able to learn non-linear input and output dependencies. Instead of depending solely on the input patterns, OA-RVM models output structure and covariances within a predefined temporal window, thus capturing past, current and future context. As a result, output patterns manifested in the training data are captured within a formal probabilistic framework, and subsequently used during inference. As a proof of concept, we target the highly challenging problem of dimensional and continuous prediction of emotions from naturalistic facial expressions. We demonstrate the advantages of the proposed OA-RVM regression by performing both subject-dependent and subject-independent experiments using the SAL database. The experimental results show that OA-RVM regression outperforms the traditional RVM and SVM regression approaches in prediction accuracy,generating more robust and accurate models.
    Original languageUndefined
    Title of host publicationIEEE International Conference on Automatic Face & Gesture Recognition and Workshops (FG 2011)
    Place of PublicationUSA
    PublisherIEEE Communications Society
    Pages16-23
    Number of pages8
    ISBN (Print)978-1-4244-9140-7
    DOIs
    Publication statusPublished - Mar 2011
    Event9th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2011 - Santa Barbara, United States
    Duration: 21 Mar 201125 Mar 2011
    Conference number: 9

    Publication series

    Name
    PublisherIEEE Communications Society

    Conference

    Conference9th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2011
    Abbreviated titleFG
    CountryUnited States
    CitySanta Barbara
    Period21/03/1125/03/11

    Keywords

    • METIS-285040
    • IR-79503
    • Gaussian distribution
    • Kernel
    • Noise
    • HMI-MI: MULTIMODAL INTERACTIONS
    • Feature extraction
    • Support Vector Machines
    • Training
    • EC Grant Agreement nr.: FP7/211486
    • EWI-21348
    • Estimation

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