Facial Action Unit Detection and Intensity Estimation From Self-Supervised Representation-Reference-Cited by-同舟云学术

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Facial Action Unit Detection and Intensity Estimation From Self-Supervised Representation

Published:2024-07 Issue:3 Volume:15 Page:1669-1683
ISSN:1949-3045
Container-title:IEEE Transactions on Affective Computing
language:
Short-container-title:IEEE Trans. Affective Comput.

Author:

Ma Bowen¹^ORCID,An Rudong¹^ORCID,Zhang Wei¹^ORCID,Ding Yu¹^ORCID,Zhao Zeng¹^ORCID,Zhang Rongsheng¹^ORCID,Lv Tangjie¹^ORCID,Fan Changjie¹^ORCID,Hu Zhipeng¹^ORCID

Affiliation:

1. Netease Fuxi AI Lab, Hangzhou, China

Funder

Key Research and Development Program of Zhejiang Province

2022 Key Artificial Intelligence Science and Technology Innovation Project of Hangzhou Science and Technology Office

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/5165369/10666803/10439628.pdf?arnumber=10439628

Reference111 articles.

1. Facial Action Coding System

2. Automatically Detecting Pain in Video Through Facial Action Units

3. Variations of Facial Actions While Playing Games with Inducing Boredom and Stress

4. A Novel Machine Vision-Based 3D Facial Action Unit Identification for Fatigue Detection

5. Weakly Supervised Regional and Temporal Learning for Facial Action Unit Recognition

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