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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Su Yun, H., Lixin, B.X., et al.: Knowledge modeling and emotion recognition for EEG data. Chin. Sci. Bull. 60(11), 1002–1009 (2015)
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). https://doi.org/10.1007/978-3-319-46672-9_58
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)
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)
Petrantonakis, P.C., Hadjileontiadis, L.J.: Emotion recognition from EEG using higher order crossings. IEEE Trans. Inf Technol. Biomed. 14(2), 186 (2010)
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)
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)
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)
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)
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)
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).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Zhang, S., Hu, B., Li, T., Zheng, X. (2018). A Study on Emotion Recognition Based on Hierarchical Adaboost Multi-class Algorithm. In: Vaidya, J., Li, J. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2018. Lecture Notes in Computer Science(), vol 11335. Springer, Cham. https://doi.org/10.1007/978-3-030-05054-2_8
Download citation
DOI: https://doi.org/10.1007/978-3-030-05054-2_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-05053-5
Online ISBN: 978-3-030-05054-2
eBook Packages: Computer ScienceComputer Science (R0)