el 20(19): e4

Research Article

Facial emotion recognition via stationary wavelet entropy and Biogeography-based optimization

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  • @ARTICLE{10.4108/eai.30-10-2018.165702,
        author={Xiang Li and Chaosheng Tang and Junding Sun},
        title={Facial emotion recognition via stationary wavelet entropy and Biogeography-based optimization},
        journal={EAI Endorsed Transactions on e-Learning},
        volume={6},
        number={19},
        publisher={EAI},
        journal_a={EL},
        year={2020},
        month={7},
        keywords={biogeography-based optimization, facial emotion recognition,single hidden layer feedforward neural network,stationary wavelet entropy},
        doi={10.4108/eai.30-10-2018.165702}
    }
    
  • Xiang Li
    Chaosheng Tang
    Junding Sun
    Year: 2020
    Facial emotion recognition via stationary wavelet entropy and Biogeography-based optimization
    EL
    EAI
    DOI: 10.4108/eai.30-10-2018.165702
Xiang Li1, Chaosheng Tang1, Junding Sun1,*
  • 1: College of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, Henan 454000, PR China
*Contact email: sunjd@hpu.edu.cn

Abstract

INTRODUCTION: As one of the important research directions in the field of computer vision, facial emotion recognition plays an important role in people's daily life. How to make the computer accurately read facial emotion is an important research content.

OBJECTIVES: In the current research on facial emotion recognition, there are some problems such as poor generalization ability of network model and low robustness of recognition system. To solve above problems, we propose a novel facial emotion recognition method.

METHODS: Our method of feature extraction using the stationary wavelet entropy, which combines single hidden layer feedforward neural network with biogeography-based optimization for facial emotion recognition.

RESULTS: The simulation results show that the overall accuracy of our method is 93.79±1.24%.

CONCLUSION: This model is superior to the current mainstream facial emotion recognition models in the performance of facial emotion detection. In future research, we will try deep learning and other training methods.