| Summary: | 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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