Research of Facial Expression Recognition Based on Deep Learning

被引：0
|
作者：
Zhang, Linhao [1 ]
Yang, Yuliang [1 ]
Li, Wanchong [1 ]
Dang, Shuai [1 ]
Zhu, Mengyu [2 ]
机构：
[1] Univ Sci & Technol Beijing, Dept Commun Engn, Beijing, Peoples R China
[2] Beijing Inst Technol, Dept Biomed Engn, Beijing, Peoples R China
来源：
PROCEEDINGS OF 2018 IEEE 9TH INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING AND SERVICE SCIENCE (ICSESS) | 2018年
关键词：
Flili; depth-wise separable convolution; residual block; FERNet;
D O I：
暂无
中图分类号：
TP31 [计算机软件];
学科分类号：
081202 ; 0835 ;
摘要：
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 depth wise 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.
引用
收藏
页码：688 / 691
页数：4
相关论文
共 50 条
  • [1] Facial expression recognition based on deep learning
    Ge, Huilin
    Zhu, Zhiyu
    Dai, Yuewei
    Wang, Biao
    Wu, Xuedong
    COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, 2022, 215
  • [2] Dynamic Facial Expression Recognition Based on Deep Learning
    Deng, Liwei
    Wang, Qian
    Yuan, Ding
    14TH INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE AND EDUCATION (ICCSE 2019), 2019, : 32 - 37
  • [3] A survey of facial expression recognition based on deep learning
    Wei, Heng
    Zhang, Zhi
    PROCEEDINGS OF THE 15TH IEEE CONFERENCE ON INDUSTRIAL ELECTRONICS AND APPLICATIONS (ICIEA 2020), 2020, : 90 - 94
  • [4] Facial Expression Recognition Using Deep Learning
    Shehu, Harisu Abdullahi
    Sharif, Md Haidar
    Uyaver, Sahin
    FOURTH INTERNATIONAL CONFERENCE OF MATHEMATICAL SCIENCES (ICMS 2020), 2021, 2334
  • [5] Facial Expression Recognition via Deep Learning
    Fathallah, Abir
    Abdi, Lotfi
    Douik, Ali
    2017 IEEE/ACS 14TH INTERNATIONAL CONFERENCE ON COMPUTER SYSTEMS AND APPLICATIONS (AICCSA), 2017, : 745 - 750
  • [6] Facial Expression Recognition via Deep Learning
    Zhao, Xiaoming
    Shi, Xugan
    Zhang, Shiqing
    IETE TECHNICAL REVIEW, 2015, 32 (05) : 347 - 355
  • [7] Deep Learning Models for Facial Expression Recognition
    Sajjanhar, Atul
    Wu, ZhaoQi
    Wen, Quan
    2018 INTERNATIONAL CONFERENCE ON DIGITAL IMAGE COMPUTING: TECHNIQUES AND APPLICATIONS (DICTA), 2018, : 583 - 588
  • [8] Deep Learning Based Transfer Learning for Possible Facial Psychological Expression Recognition
    Li, Mi
    Cao, Lei
    Liu, Dachao
    Li, Leilei
    Lu, Shengfu
    JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS, 2018, 8 (07) : 1478 - 1485
  • [9] Deep Learning Methods for Facial Expression Recognition
    Refat, Chowdhury Mohammad Masum
    Azlan, Norsinnira Zainul
    2019 7TH INTERNATIONAL CONFERENCE ON MECHATRONICS ENGINEERING (ICOM), 2019, : 118 - 123
  • [10] Facial expression recognition via deep learning
    Lv, Yadan
    Feng, Zhiyong
    Xu, Chao
    2014 INTERNATIONAL CONFERENCE ON SMART COMPUTING (SMARTCOMP), 2014,
12345