Research Article
Facial Expression Recognition via ResNet-18
@INPROCEEDINGS{10.1007/978-3-030-82565-2_24, author={Bin Li and Runda Li and Dimas Lima}, title={Facial Expression Recognition via ResNet-18}, proceedings={Multimedia Technology and Enhanced Learning. Third EAI International Conference, ICMTEL 2021, Virtual Event, April 8--9, 2021, Proceedings, Part II}, proceedings_a={ICMTEL PART 2}, year={2021}, month={7}, keywords={Deep residual network Facial expression recognition ResNet-18}, doi={10.1007/978-3-030-82565-2_24} }- Bin Li
Runda Li
Dimas Lima
Year: 2021
Facial Expression Recognition via ResNet-18
ICMTEL PART 2
Springer
DOI: 10.1007/978-3-030-82565-2_24
Abstract
As an important part of human-computer interaction, facial expression recognition has become a hot research topic in the fields of computer vision, pattern recognition, artificial intelligence, etc., and plays an important role in our daily life. With the development of deep learning and convolutional neural network, the research of facial expression recognition has also made great progress. Moreover, in the current face emotion recognition research, there are problems such as poor generalization ability of network model. The extraction of traditional facial expression recognition features is complex and the effect is not ideal. In order to improve the effect of facial expression recognition, we propose a feature extraction method for deep residual network, and use deep residual network ResNet-18 to extract the features of the data set. Through the experimental simulation of the specified data set, it can be proved that this model is superior to state-of-the-art methods model.
