Weakly-Supervised Facial Expression Recognition in the Wild With Noisy Data

doi:10.1109/TMM.2021.3072786

	Weakly-Supervised Facial Expression Recognition in the Wild With Noisy Data
	Zhang, Feifei 1; Xu, Mingliang 2; Xu, Changsheng1,3,4
发表期刊	IEEE TRANSACTIONS ON MULTIMEDIA
ISSN	1520-9210
	2022
卷号	24 页码:1800-1814
通讯作者	Xu, Changsheng(csxu@nlpr.ia.ac.cn)
摘要	Facial expression recognition (FER) has attracted much attention in recent years due to its wide applications. While some progress has been achieved thanks to the emergence of deep learning, the challenge occasioned by pose variations remains. Therefore, most conventional approaches mainly perform FER under laboratory-controlled environment, and the FER in-the-wild has received relatively less attention. To implement the FER in-the-wild, the pose-invariant expression recognition model would be a possible solution but for a paucity of training data. Sufficient training data with reliable expression labels on FER tasks typically are unavailable. This paper devotes to addressing the problem of how to model pose variations in facial images, and how to leverage noisy data in the web to boost the FER performance. The proposed model is implemented in an end-to-end weakly supervised manner and enjoys several merits. First, the proposed model utilizes massive noisy labeled data to boost the performance of the FER classifier trained on a small set of clean labels. Second, we offer a novel pose modeling network to adaptively capture the discrepancy in the deep representation space of facial images under different head poses, and consequently, the pose-invariant expression representations can be learned in our model. Last, to exploit the reliable information in the noisy data, we formulate a noise modeling network, which is capable of learning the mapping from feature space to the residuals between clean labels and noisy labels. We validate the proposed approach on four public FER benchmarks: AffectNet, RAF-DB, SFEW, and BU-3DFE. Extensive experiments show that the proposed method performs favorably against state-of-the-art methods.
关键词	Noise measurement Face recognition Data models Task analysis Training data Training Annotations Facial expression recognition noisy labeled data clean labels end-to-end pose modeling noise modeling
DOI	10.1109/TMM.2021.3072786
关键词[WOS]	MULTIVIEW ; POSE
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Program of China[2017YFB1002804] ; National Natural Science Foundation of China[61720106006] ; National Natural Science Foundation of China[62002355] ; National Natural Science Foundation of China[61721004] ; National Natural Science Foundation of China[61832002] ; National Natural Science Foundation of China[61532009] ; National Natural Science Foundation of China[U1705262] ; National Natural Science Foundation of China[U1836220] ; National Natural Science Foundation of China[61751211] ; National Natural Science Foundation of China[62072455] ; Key Research Program of Frontier Sciences, CAS[QYZDJ-SSW-JSC039] ; National Postdoctoral Program for Innovative Talents[BX20190367] ; Jiangsu Province key research and development plan[BE2020036]
项目资助者	National Key Research and Development Program of China ; National Natural Science Foundation of China ; Key Research Program of Frontier Sciences, CAS ; National Postdoctoral Program for Innovative Talents ; Jiangsu Province key research and development plan
WOS研究方向	Computer Science ; Telecommunications
WOS类目	Computer Science, Information Systems ; Computer Science, Software Engineering ; Telecommunications
WOS记录号	WOS:000778959200005
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
七大方向——子方向分类	图像视频处理与分析
引用统计	被引频次：23[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/48371
专题	多模态人工智能系统全国重点实验室_多媒体计算
通讯作者	Xu, Changsheng
作者单位	1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 2.Zhengzhou Univ, Sch Informat Engn, Zhengzhou 450000, Peoples R China 3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 4.Peng Cheng Lab, Shenzhen 518066, Peoples R China
第一作者单位	模式识别国家重点实验室
通讯作者单位	模式识别国家重点实验室
推荐引用方式 GB/T 7714	Zhang, Feifei,Xu, Mingliang,Xu, Changsheng. Weakly-Supervised Facial Expression Recognition in the Wild With Noisy Data[J]. IEEE TRANSACTIONS ON MULTIMEDIA,2022,24:1800-1814.
APA	Zhang, Feifei,Xu, Mingliang,&Xu, Changsheng.(2022).Weakly-Supervised Facial Expression Recognition in the Wild With Noisy Data.IEEE TRANSACTIONS ON MULTIMEDIA,24,1800-1814.
MLA	Zhang, Feifei,et al."Weakly-Supervised Facial Expression Recognition in the Wild With Noisy Data".IEEE TRANSACTIONS ON MULTIMEDIA 24(2022):1800-1814.