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    Automatic 4D facial expression recognition using DCT features

    Access Status
    Fulltext not available
    Authors
    Xue, M.
    Mian, A.
    Liu, Wan-Quan
    Li, Ling
    Date
    2015
    Type
    Conference Paper
    
    Metadata
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    Citation
    Xue, M. and Mian, A. and Liu, W. and Li, L. 2015. Automatic 4D facial expression recognition using DCT features, in Proceedings of the 2015 IEEE Winter Conference on Applications of Computer Vision, Jan 5-9 2015, pp. 199-206. Waikoloa, Hl: Institute of Electrical and Electronics Engineers Inc.
    Source Title
    Proceedings - 2015 IEEE Winter Conference on Applications of Computer Vision, WACV 2015
    DOI
    10.1109/WACV.2015.34
    ISBN
    9781479966820
    School
    Department of Computing
    URI
    http://hdl.handle.net/20.500.11937/28970
    Collection
    • Curtin Research Publications
    Abstract

    This paper addresses the problem of person-independent 4D facial expression recognition. Unlike the majority of existing works, we propose to extract spatio-temporal features in 4D data (3D expression sequences changing over time) to represent 3D facial expression dynamics sufficiently, rather than extracting features frame-by-frame. First, the proposed method extracts local depth patch-sequences from consecutive expression frames based on the automatically detected facial landmarks. Three dimension discrete cosine transform (3D-DCT) is then applied on these patch-sequences to extract spatio-temporal features for facial expression dynamic representation. Finally, the extracted compact features (3D-DCT coefficients) are fed to nearest-neighbor classifier to finish expression recognition after feature selection and dimension reduction, in which the redundant features are filtered out. Experiments on the benchmark BU-4DFE database show that the proposed method achieves the best average recognition rate 78.8% among the existing automatic approaches, and outperforms the existing techniques in the recognition of those easily confused expressions (anger and sadness) significantly.

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