Abstract
A network model for facial expression recognition is designed and named DI-FERNet in this paper. The network uses depth-wise separable convolution, dilated convolution and residual module to build the network structure. This paper uses MTCNN to perform face alignment processing on the pictures in the dataset. A large number of experiments are carried out on the selected expression datasets KDEF and RAF. The test accuracy on KDEF is 97.2% and on the RAF is 77.1%.
Original language | English |
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Title of host publication | 2019 28th Wireless and Optical Communications Conference, WOCC 2019 - Proceedings |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
ISBN (Electronic) | 9781728106601 |
DOIs | |
Publication status | Published - May 2019 |
Event | 28th Wireless and Optical Communications Conference, WOCC 2019 - Beijing, China Duration: 9 May 2019 → 10 May 2019 |
Publication series
Name | 2019 28th Wireless and Optical Communications Conference, WOCC 2019 - Proceedings |
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Conference
Conference | 28th Wireless and Optical Communications Conference, WOCC 2019 |
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Country/Territory | China |
City | Beijing |
Period | 9/05/19 → 10/05/19 |
Keywords
- Depth-wise separable convolution
- Dilated convolution
- FER
- MTCNN
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Zhang, X., Yang, Y., Zhang, L., Li, W., Dang, S., Wang, P., & Zhu, M. (2019). Research on facial expression recognition algorithm based on convolutional neural network. In 2019 28th Wireless and Optical Communications Conference, WOCC 2019 - Proceedings Article 8770616 (2019 28th Wireless and Optical Communications Conference, WOCC 2019 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/WOCC.2019.8770616