Surface Normals with Modular Approach and Weighted Voting Scheme in 3D Facial Expression Classification
A crucial part for facial expression analysis is to capture a face deformation. In this work, we are interested by the employment of 3D facial surface normals (3DFSN) to classify six basic facial expressions and the proposed approach was employed on the Bosphorus database. We constructed a Principal...
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| Format: | Article |
| Language: | English |
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International Journal of Computer and Information Technology
2014
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| Subjects: | |
| Online Access: | http://ir.unimas.my/id/eprint/5222/ http://ir.unimas.my/id/eprint/5222/1/Hamimah%20Ujir.pdf |
| _version_ | 1848835613127606272 |
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| author | Hamimah, Ujir Spann, Michael |
| author_facet | Hamimah, Ujir Spann, Michael |
| author_sort | Hamimah, Ujir |
| building | UNIMAS Institutional Repository |
| collection | Online Access |
| description | A crucial part for facial expression analysis is to capture a face deformation. In this work, we are interested by the employment of 3D facial surface normals (3DFSN) to classify six basic facial expressions and the proposed approach was employed on the Bosphorus database. We constructed a Principal Component Analysis (PCA) to capture variations in facial shape due to changes in expressions using 3DFSN as the feature vector. A modular approach is employed where a face is decomposed into six different regions and the expression classification for each module is carried out independently. We constructed a Weighted Voting Scheme (WVS) to infer the emotion underlying a collection of modules using a weight that is determined using the AdaBoost learning algorithm. Our results indicate that using 3DFSN as the feature vector of WVS yields a better performance than 3D facial points and 3D facial distance measurements in facial expression classification using both WVS and a Majority Voting Scheme (MVS). Our work is different with the existing works as they used the dataset with facial intensity information while we used dataset with no intensity. New insight in facial expression analysis is found particularly when no intensity information is provided. Surface normals does has a potential to be used as the feature vectors to classify six basic expressions. |
| first_indexed | 2025-11-15T06:10:39Z |
| format | Article |
| id | unimas-5222 |
| institution | Universiti Malaysia Sarawak |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T06:10:39Z |
| publishDate | 2014 |
| publisher | International Journal of Computer and Information Technology |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | unimas-52222022-09-15T03:04:48Z http://ir.unimas.my/id/eprint/5222/ Surface Normals with Modular Approach and Weighted Voting Scheme in 3D Facial Expression Classification Hamimah, Ujir Spann, Michael T Technology (General) TK Electrical engineering. Electronics Nuclear engineering A crucial part for facial expression analysis is to capture a face deformation. In this work, we are interested by the employment of 3D facial surface normals (3DFSN) to classify six basic facial expressions and the proposed approach was employed on the Bosphorus database. We constructed a Principal Component Analysis (PCA) to capture variations in facial shape due to changes in expressions using 3DFSN as the feature vector. A modular approach is employed where a face is decomposed into six different regions and the expression classification for each module is carried out independently. We constructed a Weighted Voting Scheme (WVS) to infer the emotion underlying a collection of modules using a weight that is determined using the AdaBoost learning algorithm. Our results indicate that using 3DFSN as the feature vector of WVS yields a better performance than 3D facial points and 3D facial distance measurements in facial expression classification using both WVS and a Majority Voting Scheme (MVS). Our work is different with the existing works as they used the dataset with facial intensity information while we used dataset with no intensity. New insight in facial expression analysis is found particularly when no intensity information is provided. Surface normals does has a potential to be used as the feature vectors to classify six basic expressions. International Journal of Computer and Information Technology 2014 Article PeerReviewed text en http://ir.unimas.my/id/eprint/5222/1/Hamimah%20Ujir.pdf Hamimah, Ujir and Spann, Michael (2014) Surface Normals with Modular Approach and Weighted Voting Scheme in 3D Facial Expression Classification. International Journal of Computer and Information Technology, 03 (05). ISSN 2279 – 0764 |
| spellingShingle | T Technology (General) TK Electrical engineering. Electronics Nuclear engineering Hamimah, Ujir Spann, Michael Surface Normals with Modular Approach and Weighted Voting Scheme in 3D Facial Expression Classification |
| title | Surface Normals with Modular Approach and Weighted Voting Scheme in 3D Facial Expression Classification |
| title_full | Surface Normals with Modular Approach and Weighted Voting Scheme in 3D Facial Expression Classification |
| title_fullStr | Surface Normals with Modular Approach and Weighted Voting Scheme in 3D Facial Expression Classification |
| title_full_unstemmed | Surface Normals with Modular Approach and Weighted Voting Scheme in 3D Facial Expression Classification |
| title_short | Surface Normals with Modular Approach and Weighted Voting Scheme in 3D Facial Expression Classification |
| title_sort | surface normals with modular approach and weighted voting scheme in 3d facial expression classification |
| topic | T Technology (General) TK Electrical engineering. Electronics Nuclear engineering |
| url | http://ir.unimas.my/id/eprint/5222/ http://ir.unimas.my/id/eprint/5222/1/Hamimah%20Ujir.pdf |