Facial Expressions Recognition Based on Cognition and Mapped Binary Patterns
In this paper, a new expression recognition approach is presented based on cognition and mapped binary patterns. At first, the approach is based on the LBP operator to extract the facial contours. Secondly, the establishment of pseudo-3-D model is used to segment the face area into six facial expres...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2018-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8322125/ |
| _version_ | 1818853867585011712 |
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| author | Chao Qi Min Li Qiushi Wang Huiquan Zhang Jinling Xing Zhifan Gao Huailing Zhang |
| author_facet | Chao Qi Min Li Qiushi Wang Huiquan Zhang Jinling Xing Zhifan Gao Huailing Zhang |
| author_sort | Chao Qi |
| collection | DOAJ |
| description | In this paper, a new expression recognition approach is presented based on cognition and mapped binary patterns. At first, the approach is based on the LBP operator to extract the facial contours. Secondly, the establishment of pseudo-3-D model is used to segment the face area into six facial expression sub-regions. In this context, the sub-regions and the global facial expression images use the mapped LBP method for feature extraction, and then use two classifications which are the support vector machine and softmax with two kinds of emotion classification models the basic emotion model and the circumplex emotion model. At last, we perform a comparative experiment on the expansion of the Cohn-Kanade (CK +) facial expression data set and the test data sets collected from ten volunteers. The experimental results show that the method can effectively remove the confounding factors in the image. And the result of using the circumplex emotion model is obviously better than the traditional emotional model. By referring to relevant studies of human cognition, we verified that eyes and mouth express more emotion. |
| first_indexed | 2024-12-19T07:43:38Z |
| format | Article |
| id | doaj.art-56178d6f7a3e4838bdf2d41fefb264a4 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-19T07:43:38Z |
| publishDate | 2018-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-56178d6f7a3e4838bdf2d41fefb264a42022-12-21T20:30:24ZengIEEEIEEE Access2169-35362018-01-016187951880310.1109/ACCESS.2018.28160448322125Facial Expressions Recognition Based on Cognition and Mapped Binary PatternsChao Qi0Min Li1Qiushi Wang2Huiquan Zhang3Jinling Xing4Zhifan Gao5https://orcid.org/0000-0002-1576-4439Huailing Zhang6Key Laboratory of Modern Teaching Technology of Ministry of Education, Engineering Laboratory of Teaching Information Technology of Shaanxi Province, School of Computer Science, Shaanxi Normal University, Xi’an, ChinaKey Laboratory of Modern Teaching Technology of Ministry of Education, Engineering Laboratory of Teaching Information Technology of Shaanxi Province, School of Computer Science, Shaanxi Normal University, Xi’an, ChinaAVIC Beijing Aeronautical Manufacturing Technology Research Institute, Beijing, ChinaKey Laboratory of Modern Teaching Technology of Ministry of Education, Engineering Laboratory of Teaching Information Technology of Shaanxi Province, School of Computer Science, Shaanxi Normal University, Xi’an, ChinaDépartement de génie informatique et génie logiciel, École Polytechnique de Montréal, Montréal, QC, CanadaShenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, ChinaInformation Engineering School, Guangzhou Medical University, Guangzhou, ChinaIn this paper, a new expression recognition approach is presented based on cognition and mapped binary patterns. At first, the approach is based on the LBP operator to extract the facial contours. Secondly, the establishment of pseudo-3-D model is used to segment the face area into six facial expression sub-regions. In this context, the sub-regions and the global facial expression images use the mapped LBP method for feature extraction, and then use two classifications which are the support vector machine and softmax with two kinds of emotion classification models the basic emotion model and the circumplex emotion model. At last, we perform a comparative experiment on the expansion of the Cohn-Kanade (CK +) facial expression data set and the test data sets collected from ten volunteers. The experimental results show that the method can effectively remove the confounding factors in the image. And the result of using the circumplex emotion model is obviously better than the traditional emotional model. By referring to relevant studies of human cognition, we verified that eyes and mouth express more emotion.https://ieeexplore.ieee.org/document/8322125/Facial expressions recognitionlocal binary patternshuman cognitionemotion modelmachine learning |
| spellingShingle | Chao Qi Min Li Qiushi Wang Huiquan Zhang Jinling Xing Zhifan Gao Huailing Zhang Facial Expressions Recognition Based on Cognition and Mapped Binary Patterns IEEE Access Facial expressions recognition local binary patterns human cognition emotion model machine learning |
| title | Facial Expressions Recognition Based on Cognition and Mapped Binary Patterns |
| title_full | Facial Expressions Recognition Based on Cognition and Mapped Binary Patterns |
| title_fullStr | Facial Expressions Recognition Based on Cognition and Mapped Binary Patterns |
| title_full_unstemmed | Facial Expressions Recognition Based on Cognition and Mapped Binary Patterns |
| title_short | Facial Expressions Recognition Based on Cognition and Mapped Binary Patterns |
| title_sort | facial expressions recognition based on cognition and mapped binary patterns |
| topic | Facial expressions recognition local binary patterns human cognition emotion model machine learning |
| url | https://ieeexplore.ieee.org/document/8322125/ |
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