Fully automatic 3D facial expression recognition using local depth features
Facial expressions form a significant part of our nonverbal communications and understanding them is essential for effective human computer interaction. Due to the diversity of facial geometry and expressions, automatic expression recognition is a challenging task. This paper deals with the problem...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Conference Paper |
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Institute of Electrical and Electronics Engineers
2014
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| Subjects: | |
| Online Access: | http://hdl.handle.net/20.500.11937/45568 |
| _version_ | 1848757321935618048 |
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| author | Xue, Mingliang Mian, A. Liu, Wan-Quan Li, Ling |
| author2 | NOT FOUND |
| author_facet | NOT FOUND Xue, Mingliang Mian, A. Liu, Wan-Quan Li, Ling |
| author_sort | Xue, Mingliang |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Facial expressions form a significant part of our nonverbal communications and understanding them is essential for effective human computer interaction. Due to the diversity of facial geometry and expressions, automatic expression recognition is a challenging task. This paper deals with the problem of person-independent facial expression recognition from a single 3D scan. We consider only the 3D shape because facial expressions are mostly encoded in facial geometry deformations rather than textures. Unlike the majority of existing works, our method is fully automatic including the detection of landmarks. We detect the four eye corners and nose tip in real time on the depth image and its gradients using Haar-like features and AdaBoost classifier. From these five points, another 25 heuristic points are defined to extract local depth features for representing facial expressions. The depth features are projected to a lower dimensional linear subspace where feature selection is performed by maximizing their relevance and minimizing their redundancy. The selected features are then used to train a multi-class SVM for the final classification. Experiments on the benchmark BU-3DFE database show that the proposed method outperforms existing automatic techniques, and is comparable even to the approaches using manual landmarks. |
| first_indexed | 2025-11-14T09:26:15Z |
| format | Conference Paper |
| id | curtin-20.500.11937-45568 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T09:26:15Z |
| publishDate | 2014 |
| publisher | Institute of Electrical and Electronics Engineers |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-455682017-09-13T14:26:48Z Fully automatic 3D facial expression recognition using local depth features Xue, Mingliang Mian, A. Liu, Wan-Quan Li, Ling NOT FOUND Three-dimensional displays feature selection learning (artificial intelligence) feature extraction Face recognition Vectors human computer interaction Feature extraction support vector machines face recognition Nose Haar transforms Mouth image classification Facial expressions form a significant part of our nonverbal communications and understanding them is essential for effective human computer interaction. Due to the diversity of facial geometry and expressions, automatic expression recognition is a challenging task. This paper deals with the problem of person-independent facial expression recognition from a single 3D scan. We consider only the 3D shape because facial expressions are mostly encoded in facial geometry deformations rather than textures. Unlike the majority of existing works, our method is fully automatic including the detection of landmarks. We detect the four eye corners and nose tip in real time on the depth image and its gradients using Haar-like features and AdaBoost classifier. From these five points, another 25 heuristic points are defined to extract local depth features for representing facial expressions. The depth features are projected to a lower dimensional linear subspace where feature selection is performed by maximizing their relevance and minimizing their redundancy. The selected features are then used to train a multi-class SVM for the final classification. Experiments on the benchmark BU-3DFE database show that the proposed method outperforms existing automatic techniques, and is comparable even to the approaches using manual landmarks. 2014 Conference Paper http://hdl.handle.net/20.500.11937/45568 10.1109/WACV.2014.6835736 Institute of Electrical and Electronics Engineers restricted |
| spellingShingle | Three-dimensional displays feature selection learning (artificial intelligence) feature extraction Face recognition Vectors human computer interaction Feature extraction support vector machines face recognition Nose Haar transforms Mouth image classification Xue, Mingliang Mian, A. Liu, Wan-Quan Li, Ling Fully automatic 3D facial expression recognition using local depth features |
| title | Fully automatic 3D facial expression recognition using local depth features |
| title_full | Fully automatic 3D facial expression recognition using local depth features |
| title_fullStr | Fully automatic 3D facial expression recognition using local depth features |
| title_full_unstemmed | Fully automatic 3D facial expression recognition using local depth features |
| title_short | Fully automatic 3D facial expression recognition using local depth features |
| title_sort | fully automatic 3d facial expression recognition using local depth features |
| topic | Three-dimensional displays feature selection learning (artificial intelligence) feature extraction Face recognition Vectors human computer interaction Feature extraction support vector machines face recognition Nose Haar transforms Mouth image classification |
| url | http://hdl.handle.net/20.500.11937/45568 |