Facial Emotion Recognition Based on Biorthogonal Wavelet Entropy, Fuzzy Support Vector Machine, and Stratified Cross Validation
Emotion recognition represents the position and motion of facial muscles. It contributes significantly in many fields. Current approaches have not obtained good results. This paper aimed to propose a new emotion recognition system based on facial expression images. We enrolled 20 subjects and let ea...
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| Published in | IEEE Access Vol. 4; pp. 8375 - 8385 |
|---|---|
| Main Authors | , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Piscataway
IEEE
2016
Institute of Electrical and Electronics Engineers (IEEE) The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| Online Access | Get full text |
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| Abstract | Emotion recognition represents the position and motion of facial muscles. It contributes significantly in many fields. Current approaches have not obtained good results. This paper aimed to propose a new emotion recognition system based on facial expression images. We enrolled 20 subjects and let each subject pose seven different emotions: happy, sadness, surprise, anger, disgust, fear, and neutral. Afterward, we employed biorthogonal wavelet entropy to extract multiscale features, and used fuzzy multiclass support vector machine to be the classifier. The stratified cross validation was employed as a strict validation model. The statistical analysis showed our method achieved an overall accuracy of 96.77±0.10%. Besides, our method is superior to three state-of-the-art methods. In all, this proposed method is efficient. |
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| AbstractList | Emotion recognition represents the position and motion of facial muscles. It contributes significantly in many fields. Current approaches have not obtained good results. This paper aimed to propose a new emotion recognition system based on facial expression images. We enrolled 20 subjects and let each subject pose seven different emotions: happy, sadness, surprise, anger, disgust, fear, and neutral. Afterward, we employed biorthogonal wavelet entropy to extract multiscale features, and used fuzzy multiclass support vector machine to be the classifier. The stratified cross validation was employed as a strict validation model. The statistical analysis showed our method achieved an overall accuracy of 96.77±0.10%. Besides, our method is superior to three state-of-the-art methods. In all, this proposed method is efficient. |
| Author | Shui-Hua Wang Yu-Dong Zhang Phillips, Preetha Hui-Min Lu Qing-Ming Liu Xing-Xing Zhou Zhang-Jing Yang |
| Author_xml | – sequence: 1 surname: Yu-Dong Zhang fullname: Yu-Dong Zhang email: zhangyudong@njnu.edu.cn organization: Sch. of Technol., Nanjing Audit Univ., Nanjing, China – sequence: 2 surname: Zhang-Jing Yang fullname: Zhang-Jing Yang email: yzzjjj@126.com organization: Sch. of Technol., Nanjing Audit Univ., Nanjing, China – sequence: 3 surname: Hui-Min Lu fullname: Hui-Min Lu organization: Dept. of Mech. & Control Eng., Kyushu Inst. of Technol., Fukuoka, Japan – sequence: 4 surname: Xing-Xing Zhou fullname: Xing-Xing Zhou organization: Sch. of Comput. Sci. & Technol., Nanjing Normal Univ., Nanjing, China – sequence: 5 givenname: Preetha surname: Phillips fullname: Phillips, Preetha organization: Sch. of Natural Sci. & Math., Shepherd Univ., Shepherdstown, WV, USA – sequence: 6 surname: Qing-Ming Liu fullname: Qing-Ming Liu organization: Sch. of Psychol., Nanjing Normal Univ., Nanjing, China – sequence: 7 surname: Shui-Hua Wang fullname: Shui-Hua Wang email: wangshuihua@njnu.edu.cn organization: Sch. of Comput. Sci. & Technol., Nanjing Normal Univ., Nanjing, China |
| BackLink | https://cir.nii.ac.jp/crid/1870583642594545792$$DView record in CiNii |
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| Snippet | Emotion recognition represents the position and motion of facial muscles. It contributes significantly in many fields. Current approaches have not obtained... |
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| SubjectTerms | biorthogonal wavelet entropy Electrical engineering. Electronics. Nuclear engineering Emotion recognition Emotions Entropy Face recognition Facial emotion recognition facial expression Feature extraction Fuzzy logic Low-pass filters Muscles Object recognition Statistical analysis support vector machine Support vector machines TK1-9971 Wavelet transforms |
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| Title | Facial Emotion Recognition Based on Biorthogonal Wavelet Entropy, Fuzzy Support Vector Machine, and Stratified Cross Validation |
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