@inproceedings{471ee624045c4d7390dea9234935c805,
title = "Research of Facial Expression Recognition Based on Deep Learning",
abstract = "This paper proposes a convolutional neural network for facial expression recognition (FER) based on deep learning, named FERNet. FERNet contains 4 residual depth-wise separable convolution modules., each of which includes 3 depthwise separable convolution layers and 1 standard convolution layer. It is a fully convolutional neural network that replaces the fully connected layer with global average pool (GAP) layer. The results show that the average accuracy of FERNet in the KDEF dataset is 93.7%, and the average accuracy of the RAF dataset is 71.9%. Compared with other networks and methods, FERNet has a better performance in facial expression recognition.",
keywords = "FER, FERNet, depth-wise separable convolution, residual block",
author = "Linhao Zhang and Yuliang Yang and Wanchong Li and Shuai Dang and Mengyu Zhu",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 9th IEEE International Conference on Software Engineering and Service Science, ICSESS 2018 ; Conference date: 23-11-2018 Through 25-11-2018",
year = "2018",
month = jul,
day = "2",
doi = "10.1109/ICSESS.2018.8663777",
language = "English",
series = "Proceedings of the IEEE International Conference on Software Engineering and Service Sciences, ICSESS",
publisher = "IEEE Computer Society",
pages = "688--691",
editor = "Li Wenzheng and Babu, {M. Surendra Prasad}",
booktitle = "ICSESS 2018 - Proceedings of 2018 IEEE 9th International Conference on Software Engineering and Service Science",
address = "United States",
}