Recognition of 3D facial expression dynamics

G. Sandbach, S. Zafeiriou, Maja Pantic, D. Rueckert

    Research output: Contribution to journalArticleAcademicpeer-review

    79 Citations (Scopus)

    Abstract

    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 modelled 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 GentleBoost classifiers and build a Hidden Markov Model in order to model the full temporal dynamics of the expression. The proposed fully automatic system was employed on the BU-4DFE database for distinguishing between the six universal expressions: Happy, Sad, Angry, Disgust, Surprise and Fear. Comparisons with a similar 2D 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 in a fully automatic manner.
    Original languageUndefined
    Pages (from-to)762-773
    Number of pages12
    JournalImage and vision computing
    Volume30
    Issue number10
    DOIs
    Publication statusPublished - Oct 2012

    Keywords

    • HMI-MI: MULTIMODAL INTERACTIONS
    • EWI-22938
    • 2D/3D comparison
    • 3D facial geometries
    • IR-84216
    • Facial expression recognition
    • Quad-tree decomposition
    • METIS-296241
    • Motion-based features

    Cite this

    Sandbach, G., Zafeiriou, S., Pantic, M., & Rueckert, D. (2012). Recognition of 3D facial expression dynamics. Image and vision computing, 30(10), 762-773. https://doi.org/10.1016/j.imavis.2012.01.006