Facial expression recognition via transfer learning

Li, Bin (2021) Facial expression recognition via transfer learning. EAI Endorsed Transactions on e-Learning, 7 (21). e4. ISSN 2032-9253

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Abstract

INTRODUCTION: With the development of artificial intelligence, facial expression recognition has become a hot topic. Facial expression recognition has been widely applied to every field of our life. How to improve the accuracy of facial emotion recognition is an important research content.

OBJECTIVES: In today's facial expression recognition, there are problems such as weak generalization ability and low recognition accuracy. Aiming to improve the current facial expression recognition problems, we propose a novel facial emotion recognition method.

METHODS: This paper focuses on the deep learning-based static face image expression recognition method, and combines transfer learning and deep residual network ResNet-101 to realize facial expression recognition.

RESULTS: The simulation results show that the overall accuracy of our method is 96.29± 0.78%.

CONCLUSION: The performance of this model is superior to the current mainstream face emotion recognition models. In the future research, we will try other methods based on deep learning.

Item Type: Article
Uncontrolled Keywords: Deep residual network, Facial expression recognition, ResNet-101, Transfer learning
Subjects: H Social Sciences > H Social Sciences (General)
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
QA75 Electronic computers. Computer science
Depositing User: EAI Editor IV
Date Deposited: 09 Jul 2021 08:28
Last Modified: 09 Jul 2021 08:28
URI: https://eprints.eudl.eu/id/eprint/4351

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