A Study on Emotion Recognition Based on Hierarchical Adaboost Multi-class Algorithm

  • Song Zhang
  • Bin HuEmail author
  • Tiantian Li
  • Xiangwei Zheng
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11335)


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.


Emotion 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).


  1. 1.
    Su Yun, H., Lixin, B.X., et al.: Knowledge modeling and emotion recognition for EEG data. Chin. Sci. Bull. 60(11), 1002–1009 (2015)CrossRefGoogle Scholar
  2. 2.
    Liu, W., Zheng, W.-L., Lu, B.-L.: Emotion recognition using multimodal deep learning. In: Hirose, A., Ozawa, S., Doya, K., Ikeda, K., Lee, M., Liu, D. (eds.) ICONIP 2016. LNCS, vol. 9948, pp. 521–529. Springer, Cham (2016). Scholar
  3. 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. 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
  5. 5.
    Petrantonakis, P.C., Hadjileontiadis, L.J.: Emotion recognition from EEG using higher order crossings. IEEE Trans. Inf Technol. Biomed. 14(2), 186 (2010)CrossRefGoogle Scholar
  6. 6.
    Murugappan, M., Nagarajan, R., Yaacob, S.: Combining spatial filtering and wavelet transform for classifying human emotions using EEG signals. J. Med. Biol. Eng. 31(1), 45–51 (2011)CrossRefGoogle Scholar
  7. 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. 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. 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. 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

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Song Zhang
    • 1
    • 2
  • Bin Hu
    • 1
    • 2
    Email author
  • Tiantian Li
    • 3
  • Xiangwei Zheng
    • 1
    • 2
  1. 1.School of Information Science and EngineeringShandong Normal UniversityJi’nanChina
  2. 2.Shandong Provincial Key Laboratory for Distributed Computer Software Novel TechnologyJi’nanChina
  3. 3.Faculty of EducationShandong Normal UniversityJi’nanChina

Personalised recommendations