Facial emotion recognition via stationary wavelet entropy and Biogeography-based optimization
INTRODUCTION: As one of the important research directions in the field of computer vision, facial emotion recognition plays an important role in people's daily life. How to make the computer accurately read facial emotion is an important research content. OBJECTIVES: In the current research...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
European Alliance for Innovation (EAI)
2020-08-01
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| Series: | EAI Endorsed Transactions on e-Learning |
| Subjects: | |
| Online Access: | https://eudl.eu/pdf/10.4108/eai.30-10-2018.165702 |
| _version_ | 1818176237547290624 |
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| author | Xiang Li Chaosheng Tang Junding Sun |
| author_facet | Xiang Li Chaosheng Tang Junding Sun |
| author_sort | Xiang Li |
| collection | DOAJ |
| description | INTRODUCTION: As one of the important research directions in the field of computer vision, facial emotion recognition plays an important role in people's daily life. How to make the computer accurately read facial emotion is an important research content. OBJECTIVES: In the current research on facial emotion recognition, there are some problems such as poor generalization ability of network model and low robustness of recognition system. To solve above problems, we propose a novel facial emotion recognition method. METHODS: Our method of feature extraction using the stationary wavelet entropy, which combines single hidden layer feedforward neural network with biogeography-based optimization for facial emotion recognition. RESULTS: The simulation results show that the overall accuracy of our method is 93.79±1.24%. CONCLUSION: This model is superior to the current mainstream facial emotion recognition models in the performance of facial emotion detection. In future research, we will try deep learning and other training methods. |
| first_indexed | 2024-12-11T20:13:00Z |
| format | Article |
| id | doaj.art-bb3d1e18f1b1422b92ff1ae955b02686 |
| institution | Directory Open Access Journal |
| issn | 2032-9253 |
| language | English |
| last_indexed | 2024-12-11T20:13:00Z |
| publishDate | 2020-08-01 |
| publisher | European Alliance for Innovation (EAI) |
| record_format | Article |
| series | EAI Endorsed Transactions on e-Learning |
| spelling | doaj.art-bb3d1e18f1b1422b92ff1ae955b026862022-12-22T00:52:15ZengEuropean Alliance for Innovation (EAI)EAI Endorsed Transactions on e-Learning2032-92532020-08-0161910.4108/eai.30-10-2018.165702Facial emotion recognition via stationary wavelet entropy and Biogeography-based optimizationXiang Li0Chaosheng Tang1Junding Sun2College of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, Henan 454000, PR ChinaCollege of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, Henan 454000, PR ChinaCollege of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, Henan 454000, PR ChinaINTRODUCTION: As one of the important research directions in the field of computer vision, facial emotion recognition plays an important role in people's daily life. How to make the computer accurately read facial emotion is an important research content. OBJECTIVES: In the current research on facial emotion recognition, there are some problems such as poor generalization ability of network model and low robustness of recognition system. To solve above problems, we propose a novel facial emotion recognition method. METHODS: Our method of feature extraction using the stationary wavelet entropy, which combines single hidden layer feedforward neural network with biogeography-based optimization for facial emotion recognition. RESULTS: The simulation results show that the overall accuracy of our method is 93.79±1.24%. CONCLUSION: This model is superior to the current mainstream facial emotion recognition models in the performance of facial emotion detection. In future research, we will try deep learning and other training methods.https://eudl.eu/pdf/10.4108/eai.30-10-2018.165702biogeography-based optimizationfacial emotion recognition,single hidden layer feedforward neural network,stationary wavelet entropy |
| spellingShingle | Xiang Li Chaosheng Tang Junding Sun Facial emotion recognition via stationary wavelet entropy and Biogeography-based optimization EAI Endorsed Transactions on e-Learning biogeography-based optimization facial emotion recognition,single hidden layer feedforward neural network,stationary wavelet entropy |
| title | Facial emotion recognition via stationary wavelet entropy and Biogeography-based optimization |
| title_full | Facial emotion recognition via stationary wavelet entropy and Biogeography-based optimization |
| title_fullStr | Facial emotion recognition via stationary wavelet entropy and Biogeography-based optimization |
| title_full_unstemmed | Facial emotion recognition via stationary wavelet entropy and Biogeography-based optimization |
| title_short | Facial emotion recognition via stationary wavelet entropy and Biogeography-based optimization |
| title_sort | facial emotion recognition via stationary wavelet entropy and biogeography based optimization |
| topic | biogeography-based optimization facial emotion recognition,single hidden layer feedforward neural network,stationary wavelet entropy |
| url | https://eudl.eu/pdf/10.4108/eai.30-10-2018.165702 |
| work_keys_str_mv | AT xiangli facialemotionrecognitionviastationarywaveletentropyandbiogeographybasedoptimization AT chaoshengtang facialemotionrecognitionviastationarywaveletentropyandbiogeographybasedoptimization AT jundingsun facialemotionrecognitionviastationarywaveletentropyandbiogeographybasedoptimization |