Research on facial expression recognition algorithm based on convolutional neural network

Xiaobo Zhang; Yuliang Yang; Linhao Zhang; Wanchong Li; Shuai Dang; Peng Wang; Mengyu Zhu

doi:10.1109/WOCC.2019.8770616

Research on facial expression recognition algorithm based on convolutional neural network

Xiaobo Zhang, Yuliang Yang^*, Linhao Zhang, Wanchong Li, Shuai Dang, Peng Wang, Mengyu Zhu

^*Corresponding author for this work

School of Medical and Technology

University of Science and Technology Beijing

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Citations (Scopus)

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
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	https://doi.org/10.1109/WOCC.2019.8770616
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

Conference

Conference	28th Wireless and Optical Communications Conference, WOCC 2019
Country/Territory	China
City	Beijing
Period	9/05/19 → 10/05/19

Keywords

Depth-wise separable convolution
Dilated convolution
FER
MTCNN

Access to Document

10.1109/WOCC.2019.8770616

Cite this

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

Zhang, Xiaobo ; Yang, Yuliang ; Zhang, Linhao et al. / Research on facial expression recognition algorithm based on convolutional neural network. 2019 28th Wireless and Optical Communications Conference, WOCC 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. (2019 28th Wireless and Optical Communications Conference, WOCC 2019 - Proceedings).

@inproceedings{549a00fdb5b84147a39074000dfeda61,

title = "Research on facial expression recognition algorithm based on convolutional neural network",

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%.",

keywords = "Depth-wise separable convolution, Dilated convolution, FER, MTCNN",

author = "Xiaobo Zhang and Yuliang Yang and Linhao Zhang and Wanchong Li and Shuai Dang and Peng Wang and Mengyu Zhu",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 28th Wireless and Optical Communications Conference, WOCC 2019 ; Conference date: 09-05-2019 Through 10-05-2019",

year = "2019",

month = may,

doi = "10.1109/WOCC.2019.8770616",

language = "English",

series = "2019 28th Wireless and Optical Communications Conference, WOCC 2019 - Proceedings",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2019 28th Wireless and Optical Communications Conference, WOCC 2019 - Proceedings",

address = "United States",

}

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., 8770616, 2019 28th Wireless and Optical Communications Conference, WOCC 2019 - Proceedings, Institute of Electrical and Electronics Engineers Inc., 28th Wireless and Optical Communications Conference, WOCC 2019, Beijing, China, 9/05/19. https://doi.org/10.1109/WOCC.2019.8770616

Research on facial expression recognition algorithm based on convolutional neural network. / Zhang, Xiaobo; Yang, Yuliang; Zhang, Linhao et al.
2019 28th Wireless and Optical Communications Conference, WOCC 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. 8770616 (2019 28th Wireless and Optical Communications Conference, WOCC 2019 - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Research on facial expression recognition algorithm based on convolutional neural network

AU - Zhang, Xiaobo

AU - Yang, Yuliang

AU - Zhang, Linhao

AU - Li, Wanchong

AU - Dang, Shuai

AU - Wang, Peng

AU - Zhu, Mengyu

PY - 2019/5

Y1 - 2019/5

N2 - 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%.

AB - 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%.

KW - Depth-wise separable convolution

KW - Dilated convolution

KW - FER

KW - MTCNN

UR - http://www.scopus.com/inward/record.url?scp=85070374972&partnerID=8YFLogxK

U2 - 10.1109/WOCC.2019.8770616

DO - 10.1109/WOCC.2019.8770616

M3 - Conference contribution

AN - SCOPUS:85070374972

T3 - 2019 28th Wireless and Optical Communications Conference, WOCC 2019 - Proceedings

BT - 2019 28th Wireless and Optical Communications Conference, WOCC 2019 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 28th Wireless and Optical Communications Conference, WOCC 2019

Y2 - 9 May 2019 through 10 May 2019

ER -

Zhang X, Yang Y, Zhang L, Li W, Dang S, Wang P et al. Research on facial expression recognition algorithm based on convolutional neural network. In 2019 28th Wireless and Optical Communications Conference, WOCC 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2019. 8770616. (2019 28th Wireless and Optical Communications Conference, WOCC 2019 - Proceedings). doi: 10.1109/WOCC.2019.8770616

Research on facial expression recognition algorithm based on convolutional neural network

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Keywords

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