Title | Face2Exp: Combating Data Biases for Facial Expression Recognition |
Author | |
DOI | |
Publication Years | 2022
|
Conference Name | 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
|
ISSN | 1063-6919
|
ISBN | 978-1-6654-6947-0
|
Source Title | |
Volume | 2022-June
|
Pages | 20259-20268
|
Conference Date | 18-24 June 2022
|
Conference Place | New Orleans, LA, USA
|
Publication Place | 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA
|
Publisher | |
Abstract | Facial expression recognition (FER) is challenging due to the class imbalance caused by data collection. Existing studies tackle the data bias problem using only labeled facial expression dataset. Orthogonal to existing FER methods, we propose to utilize large unlabeled face recognition (FR) datasets to enhance FER. However, this raises another data bias problem—the distribution mismatch between FR and FER data. To combat the mismatch, we propose the Meta-Face2Exp framework, which consists of a base network and an adaptation network. The base network learns prior expression knowledge on class-balanced FER data while the adaptation network is trained to fit the pseudo labels of FR data generated by the base model. To combat the mismatch between FR and FER data, Meta-Face2Exp uses a circuit feedback mechanism, which improves the base network with the feedback from the adaptation network. Experiments show that our MetaFace2Exp achieves comparable accuracy to state-of-the-art FER methods with 10% of the labeled FER data utilized by the baselines. We also demonstrate that the circuit feedback mechanism successfully eliminates data bias. |
Keywords | |
SUSTech Authorship | First
|
Language | English
|
URL | [Source Record] |
Indexed By | |
Funding Project | Guangdong Provincial Key Laboratory[2020B121201001]
; National Natural Science Foundation of China["62176170","62066042"]
|
WOS Research Area | Computer Science
; Imaging Science & Photographic Technology
|
WOS Subject | Computer Science, Artificial Intelligence
; Imaging Science & Photographic Technology
|
WOS Accession No | WOS:000870783006010
|
EI Accession Number | 20224613119721
|
Data Source | IEEE
|
PDF url | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9879702 |
Citation statistics |
Cited Times [WOS]:10
|
Document Type | Conference paper |
Identifier | http://kc.sustech.edu.cn/handle/2SGJ60CL/406461 |
Department | Department of Computer Science and Engineering 工学院_深港微电子学院 工学院_斯发基斯可信自主研究院 |
Affiliation | 1.Research Institue of Trustworthy Autonomous Systems, Southern University of Science and Technology & Department of Computer Science and Engineering, Southern University of Science and Technology 2.JD.com, Beijing, China 3.School of Microelectronics, Southern University of Science and Technology 4.Research Institue of Trustworthy Autonomous Systems, Southern University of Science and Technology & Department of Computer Science and Engineering, Southern University of Science and Technology 5.JD.com, Beijing, China 6.School of Microelectronics, Southern University of Science and Technology |
First Author Affilication | Department of Computer Science and Engineering |
First Author's First Affilication | Department of Computer Science and Engineering |
Recommended Citation GB/T 7714 |
Dan Zeng,Zhiyuan Lin,Xiao Yan,et al. Face2Exp: Combating Data Biases for Facial Expression Recognition[C]. 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA:IEEE COMPUTER SOC,2022:20259-20268.
|
Files in This Item: | There are no files associated with this item. |
|
Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.
Edit Comment