Attention-Rectified and Texture-Enhanced Cross-Attention Transformer Feature Fusion Network for Facial Expression Recognition-Reference-Cited by-同舟云学术

Attention-Rectified and Texture-Enhanced Cross-Attention Transformer Feature Fusion Network for Facial Expression Recognition

Published:2023-12 Issue:12 Volume:19 Page:11823-11832
ISSN:1551-3203
Container-title:IEEE Transactions on Industrial Informatics
language:
Short-container-title:IEEE Trans. Ind. Inf.

Author:

Sun Mingyi¹^ORCID,Cui Weigang²^ORCID,Zhang Yue¹^ORCID,Yu Shuyue³^ORCID,Liao Xiaofeng⁴^ORCID,Hu Bin⁵^ORCID,Li Yang⁶^ORCID

Affiliation:

1. Department of Automation Science and Electrical Engineering, Beihang University, Beijing, China

2. School of Engineering Medicine, Beihang University, Beijing, China

3. Beijing Aerospace Measurement and Control Technology Co., Ltd., Beijing, China

4. College of Computer Science, Chongqing University, Chongqing, China

5. Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China

6. School of Automation Science and Electrical Engineering, Beihang University, Beijing, China

Funder

Beijing Natural Science Foundation

National Natural Science Foundation of China

Beijing Municipal Education Commission - Natural Science Foundation

Beijing United Imaging Research Institute of Intelligent Imaging Foundation

Zhejiang Laboratory's International Talent Fund for Young Professionals

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Information Systems,Control and Systems Engineering

Link

http://xplorestaging.ieee.org/ielx7/9424/10288179/10061322.pdf?arnumber=10061322

Reference51 articles.

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3. Survey on Facial Expression Recognition: History, Applications, and Challenges

4. Computerized sales assistants: The application of computer technology to measure consumer interestA conceptual framework;Shergill;J. Electron. Commerce Res.,2008

5. Deep learning based single sample face recognition: a survey

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