Research Article
Facial expression recognition via transfer learning
@ARTICLE{10.4108/eai.8-4-2021.169180, author={Bin Li}, title={Facial expression recognition via transfer learning}, journal={EAI Endorsed Transactions on e-Learning}, volume={7}, number={21}, publisher={EAI}, journal_a={EL}, year={2021}, month={4}, keywords={Deep residual network, Facial expression recognition, ResNet-101, Transfer learning}, doi={10.4108/eai.8-4-2021.169180} }- Bin Li
Year: 2021
Facial expression recognition via transfer learning
EL
EAI
DOI: 10.4108/eai.8-4-2021.169180
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.
Copyright © 2021 Bin Li et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license, which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.
