A dynamic approach to the recognition of 3D facial expressions and their temporal models

Georgia Sandbach, Stefanos Zafeiriou, Maja Pantic, Daniel Rueckert

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

    64 Citations (Scopus)


    In this paper we propose a method that exploits 3D motion-based features between frames of 3D facial geometry sequences for dynamic facial expression recognition. An expressive sequence is modeled to contain an onset followed by an apex and an offset. Feature selection methods are applied in order to extract features for each of the onset and offset segments of the expression. These features are then used to train a Hidden Markov Model in order to model the full temporal dynamics of the expression. The proposed fully automatic system was tested in a subset of the BU-4DFE database for the recognition of happiness, anger and surprise. Comparisons with a similar system based on the motion extracted from facial intensity images was also performed. The attained results suggest that the use of the 3D information does indeed improve the recognition accuracy when compared to the 2D data.
    Original languageUndefined
    Title of host publicationIEEE International Conference on Automatic Face & Gesture Recognition and Workshops (FG 2011)
    Place of PublicationUSA
    PublisherIEEE Computer Society
    Number of pages8
    ISBN (Print)978-1-4244-9140-7
    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

    PublisherIEEE Computer Society


    Conference9th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2011
    Abbreviated titleFG
    Country/TerritoryUnited States
    CitySanta Barbara


    • METIS-285046
    • IR-79508
    • Face Recognition
    • Image segmentation
    • Image sequences
    • Hidden Markov models
    • Three dimensional displays
    • Training
    • EC Grant Agreement nr.: FP7/231287
    • EWI-21357
    • Feature extraction

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