Region-based facial expression recognition in still images
In Facial Expression Recognition Systems (FERS), only particular regions of the face are utilized for discrimination. The areas of the eyes, eyebrows, nose, and mouth are the most important features in any FERS. Applying facial features descriptors such as the local binary pattern (LBP) on such area...
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| Format: | Article |
| Language: | English |
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Korea Information Processing Society
2013
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| Online Access: | http://psasir.upm.edu.my/id/eprint/30644/ http://psasir.upm.edu.my/id/eprint/30644/1/Region-based%20facial%20expression%20.pdf |
| _version_ | 1848846736822370304 |
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| author | Nagi, Gawed M. O. K. Rahmat, Rahmita Wirza Khalid, Fatimah Abdullah, Muhamad Taufik |
| author_facet | Nagi, Gawed M. O. K. Rahmat, Rahmita Wirza Khalid, Fatimah Abdullah, Muhamad Taufik |
| author_sort | Nagi, Gawed M. |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | In Facial Expression Recognition Systems (FERS), only particular regions of the face are utilized for discrimination. The areas of the eyes, eyebrows, nose, and mouth are the most important features in any FERS. Applying facial features descriptors such as the local binary pattern (LBP) on such areas results in an effective and efficient FERS. In this paper, we propose an automatic facial expression recognition system. Unlike other systems, it detects and extracts the informative and discriminant regions of the face (i.e., eyes, nose, and mouth areas) using Haar-feature based cascade classifiers and these region-based features are stored into separate image files as a preprocessing step. Then, LBP is applied to these image files for facial texture representation and a feature-vector per subject is obtained by concatenating the resulting LBP histograms of the decomposed region-based features. The one-vs-rest SVM, which is a popular multi-classification method, is employed with the Radial Basis Function (RBF) for facial expression classification. Experimental results show that this approach yields good performance for both frontal and near-frontal facial images in terms of accuracy and time complexity. Cohn-Kanade and JAFFE, which are benchmark facial expression datasets, are used to evaluate this approach. |
| first_indexed | 2025-11-15T09:07:27Z |
| format | Article |
| id | upm-30644 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T09:07:27Z |
| publishDate | 2013 |
| publisher | Korea Information Processing Society |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-306442018-10-25T06:51:10Z http://psasir.upm.edu.my/id/eprint/30644/ Region-based facial expression recognition in still images Nagi, Gawed M. O. K. Rahmat, Rahmita Wirza Khalid, Fatimah Abdullah, Muhamad Taufik In Facial Expression Recognition Systems (FERS), only particular regions of the face are utilized for discrimination. The areas of the eyes, eyebrows, nose, and mouth are the most important features in any FERS. Applying facial features descriptors such as the local binary pattern (LBP) on such areas results in an effective and efficient FERS. In this paper, we propose an automatic facial expression recognition system. Unlike other systems, it detects and extracts the informative and discriminant regions of the face (i.e., eyes, nose, and mouth areas) using Haar-feature based cascade classifiers and these region-based features are stored into separate image files as a preprocessing step. Then, LBP is applied to these image files for facial texture representation and a feature-vector per subject is obtained by concatenating the resulting LBP histograms of the decomposed region-based features. The one-vs-rest SVM, which is a popular multi-classification method, is employed with the Radial Basis Function (RBF) for facial expression classification. Experimental results show that this approach yields good performance for both frontal and near-frontal facial images in terms of accuracy and time complexity. Cohn-Kanade and JAFFE, which are benchmark facial expression datasets, are used to evaluate this approach. Korea Information Processing Society 2013-03 Article PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/30644/1/Region-based%20facial%20expression%20.pdf Nagi, Gawed M. and O. K. Rahmat, Rahmita Wirza and Khalid, Fatimah and Abdullah, Muhamad Taufik (2013) Region-based facial expression recognition in still images. Journal of Information Processing Systems, 9 (1). pp. 173-188. ISSN 1976-913X; ESSN: 2092-805X http://koreascience.or.kr/article/ArticleFullRecord.jsp?cn=E1JBB0_2013_v9n1_173 10.3745/JIPS.2013.9.1.173 |
| spellingShingle | Nagi, Gawed M. O. K. Rahmat, Rahmita Wirza Khalid, Fatimah Abdullah, Muhamad Taufik Region-based facial expression recognition in still images |
| title | Region-based facial expression recognition in still images |
| title_full | Region-based facial expression recognition in still images |
| title_fullStr | Region-based facial expression recognition in still images |
| title_full_unstemmed | Region-based facial expression recognition in still images |
| title_short | Region-based facial expression recognition in still images |
| title_sort | region-based facial expression recognition in still images |
| url | http://psasir.upm.edu.my/id/eprint/30644/ http://psasir.upm.edu.my/id/eprint/30644/ http://psasir.upm.edu.my/id/eprint/30644/ http://psasir.upm.edu.my/id/eprint/30644/1/Region-based%20facial%20expression%20.pdf |