Facial Emotion Recognition Based on Biorthogonal Wavelet Entropy, Fuzzy Support Vector Machine, and Stratified Cross Validation

• Published in: IEEE Access Vol. 4; pp. 8375 - 8385
• Main Authors: Yu-Dong Zhang, Zhang-Jing Yang, Hui-Min Lu, Xing-Xing Zhou, Phillips, Preetha, Qing-Ming Liu, Shui-Hua Wang
• 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)
• Subjects: 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
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2016.2628407

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.