Automatic 4D facial expression recognition using DCT features

M. Xue, Ajmal Mian, W. Liu, L. Li

    Research output: Chapter in Book/Conference paperConference paperpeer-review

    23 Citations (Scopus)


    © 2015 IEEE. 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.
    Original languageEnglish
    Title of host publication2015 IEEE Winter Conference on Applications of Computer Vision (WACV)
    Place of PublicationUnited States
    PublisherIEEE, Institute of Electrical and Electronics Engineers
    ISBN (Print)9781479966820
    Publication statusPublished - 2015
    Event2015 IEEE Winter Conference on Applications of Computer Vision - Waikoloa, United States
    Duration: 5 Jan 20159 Mar 2015


    Conference2015 IEEE Winter Conference on Applications of Computer Vision
    Country/TerritoryUnited States


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