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

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

Abstract

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.

Keywords

Emotion recognition Hierarchical Adaboost Multi-class Algorithm Integrated weak classifier 

Notes

Acknowledgements

National Natural Science Foundation of China (61373149) and the Taishan Scholars Program of Shandong Province, China. 2018 Shandong Social Science Planning Research Project (18CJYJ06).

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Song Zhang
    • 1
    • 2
  • Bin Hu
    • 1
    • 2
  • 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

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