A Study on Emotion Recognition Based on Hierarchical Adaboost Multi-class Algorithm
Researches on human emotion recognition have attracted more and more people’s interest. Adaboost algorithm is an integrated algorithm that constructs strong classifiers by iterative aggregation of weak classifiers. This paper proposes a hierarchical Adaboost (HAdaboost) multi-class algorithm for emotion recognition, which improves the original Adaboost algorithm. The valence and arousal in different emotional states are used as classification features, and emotion recognition is performed according to their differences. Simulation experiments on the Chinese Facial Affective Picture System (CFAPS) data set demonstrate three types of emotions and seven types of emotions can be distinguished, and the average accuracy rates are 93% and 92.4% respectively.
KeywordsEmotion recognition Hierarchical Adaboost Multi-class Algorithm Integrated weak classifier
National Natural Science Foundation of China (61373149) and the Taishan Scholars Program of Shandong Province, China. 2018 Shandong Social Science Planning Research Project (18CJYJ06).
- 3.Bui, D.T., Ho, T.C., Pradhan, B., et al.: GIS-based modeling of rainfall-induced landslides using data mining-based functional trees classifier with AdaBoost, Bagging, and MultiBoost ensemble frameworks. Environ. Earth Sci. 75(14), 1–22 (2016)Google Scholar
- 4.Khosrowabadi, R., Quek, H.C., Wahab, A., et al.: EEG-based emotion recognition using self-organizing map for boundary detection. In: International Conference on Pattern Recognition, pp. 4242–4245. IEEE (2010)Google Scholar
- 7.Zhang, Y.D., Yang, Z.J., Lu, H.M., et al.: Facial emotion recognition based on biorthogonal wavelet entropy, fuzzy support vector machine, and stratified cross validation. IEEE Access 4(99), 8375–8385 (2017)Google Scholar
- 8.Bo, C., Guangyuan, L.: Emotion recognition of surface EMG signals based on wavelet transform and neural network. J. Comput. Appl. 28(2), 333–335 (2008)Google Scholar
- 9.Xin, L., Erjuan, C., Yanxiu, T., et al.: An improved EEG feature extraction algorithm and its application in emotion recognition. J. Biomed. Eng. 4, 510–517 (2017)Google Scholar
- 10.Zhang, X., Ding, J.: An improved adaboost face detection algorithm based on the different sample weights. In: IEEE, International Conference on Computer Supported Cooperative Work in Design, pp. 436–439. IEEE (2016)Google Scholar