Automatic 4D facial expression recognition using DCT features
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...
| Main Authors: | , , , |
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| Format: | Conference Paper |
| Published: |
Institute of Electrical and Electronics Engineers Inc.
2015
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| Online Access: | http://hdl.handle.net/20.500.11937/28970 |
| _version_ | 1848752678215090176 |
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| author | Xue, M. Mian, A. Liu, Wan-Quan Li, Ling |
| author_facet | Xue, M. Mian, A. Liu, Wan-Quan Li, Ling |
| author_sort | Xue, M. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T08:12:26Z |
| format | Conference Paper |
| id | curtin-20.500.11937-28970 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T08:12:26Z |
| publishDate | 2015 |
| publisher | Institute of Electrical and Electronics Engineers Inc. |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-289702017-09-13T15:17:10Z Automatic 4D facial expression recognition using DCT features Xue, M. Mian, A. Liu, Wan-Quan Li, Ling 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. 2015 Conference Paper http://hdl.handle.net/20.500.11937/28970 10.1109/WACV.2015.34 Institute of Electrical and Electronics Engineers Inc. restricted |
| spellingShingle | Xue, M. Mian, A. Liu, Wan-Quan Li, Ling Automatic 4D facial expression recognition using DCT features |
| title | Automatic 4D facial expression recognition using DCT features |
| title_full | Automatic 4D facial expression recognition using DCT features |
| title_fullStr | Automatic 4D facial expression recognition using DCT features |
| title_full_unstemmed | Automatic 4D facial expression recognition using DCT features |
| title_short | Automatic 4D facial expression recognition using DCT features |
| title_sort | automatic 4d facial expression recognition using dct features |
| url | http://hdl.handle.net/20.500.11937/28970 |