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

Li, Xiang and Tang, Chaosheng and Sun, Junding (2020) Facial emotion recognition via stationary wavelet entropy and Biogeography-based optimization. EAI Endorsed Transactions on e-Learning, 6 (19): 4. ISSN 2032-9253

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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.

Item Type: Article
Uncontrolled Keywords: biogeography-based optimization, facial emotion recognition,single hidden layer feedforward neural network,stationary wavelet entropy
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
QA75 Electronic computers. Computer science
Depositing User: EAI Editor I.
Date Deposited: 16 Sep 2020 12:25
Last Modified: 16 Sep 2020 12:25
URI: https://eprints.eudl.eu/id/eprint/337

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