Facial expression recognition via transfer learning

Authors

DOI:

https://doi.org/10.4108/eai.8-4-2021.169180

Keywords:

Deep residual network, Facial expression recognition, ResNet-101, Transfer learning

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.

Citations
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Published

08-04-2021

How to Cite

[1]
B. Li, “Facial expression recognition via transfer learning”, EAI Endorsed Trans e-Learn, vol. 7, no. 21, p. e4, Apr. 2021.