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A universal emotion recognition method based on feature priority evaluation and classifier reinforcement

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Abstract

Emotions play an indispensable role in human behaviors, and interaction based on emotion perception is attracting more attention. A method based on feature priority evaluation and classifier reinforcement is proposed in order to improve the accuracy of four-type subject-cross emotion identification. Firstly, the mixed-cross data processing strategy is employed to reduce the sample differences of extracted features. Then the feature selection method of feature priority evaluation with symmetric uncertainty is proposed to implement feature optimization for fused multi-channel features, which can effectively achieve representation of emotion states. Finally, the classifier reinforcement method of SVM-Adaboost is suggested to improve the classification performance of conventional SVM. The database DEAP is employed to verify the validity of the proposed method. Experimental results from different point of view show that the proposed method present a good emotion identification performance with accuracy 86.44%.

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Funding

This research was supported in part by the National Natural Science Foundation of China (61773078), and Postgraduate Research & Practice Innovation Program of Jiangsu Province (KYCX20_2533).

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Authors and Affiliations

  1. School of Mechanical Engineering and Rail Transit, Changzhou University, Changzhou, China

    Lizheng Pan, Shunchao Wang, Yi Ding & Lu Zhao

  2. School of Instrument Science and Engineering, Southeast University, Nanjing, China

    Aiguo Song

Authors
  1. Lizheng Pan
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  2. Shunchao Wang
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  3. Yi Ding
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  4. Lu Zhao
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  5. Aiguo Song
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Correspondence to Lizheng Pan.

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Pan, L., Wang, S., Ding, Y. et al. A universal emotion recognition method based on feature priority evaluation and classifier reinforcement. Int. J. Mach. Learn. & Cyber. 13, 3225–3237 (2022). https://doi.org/10.1007/s13042-022-01590-y

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  • DOI: https://doi.org/10.1007/s13042-022-01590-y

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