Joint Expression Synthesis and Representation Learning for Facial Expression Recognition

doi:10.1109/TCSVT.2021.3056098

CORC > 自动化研究所 > 中国科学院自动化研究所 > 模式识别国家重点实验室 > 多媒体计算与图形学团队

	Joint Expression Synthesis and Representation Learning for Facial Expression Recognition
	Zhang, Xi 1,2; Zhang, Feifei 1; Xu, Changsheng 1,2,3
刊名	IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
	2022-03-01
卷号	32 期号:3 页码:1681-1695
关键词	Face recognition Task analysis Generative adversarial networks Image synthesis Image recognition Faces Training Facial expression recognition facial image synthesis generative adversarial network representation learning
ISSN号	1051-8215
DOI	10.1109/TCSVT.2021.3056098
通讯作者	Xu, Changsheng(csxu@nlpr.ia.ac.cn)
英文摘要	Facial expression recognition (FER) is a challenging task due to the large appearance variations and the lack of sufficient training data. Conventional deep approaches either learn a good representation through deep models or synthesize images automatically to enlarge the training set. In this paper, we perform both tasks jointly and propose an end-to-end deep model for simultaneous facial expression recognition and facial image synthesis. The proposed model is based on Generative Adversarial Network (GAN) and enjoys several merits. First, the facial image synthesis and facial expression recognition tasks can boost their performance for each other via the unified model. Second, paired images are not required in our facial image synthesis network, which makes the proposed model much more general and flexible. Meanwhile, the generated facial images largely expand the training set and ease the overfitting problem in our FER task. Third, different expressions are encoded in a disentangled manner in a latent space, which enables us to synthesize facial images with arbitrary expressions by exchanging certain parts of their latent identity features. Quantitative and qualitative evaluations on both controlled and in-the-wild FER benchmarks (Multi-PIE, MMI, and RAF-DB) demonstrate the effectiveness of our proposed method on both facial image synthesis and facial expression recognition task.
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[61721004] ; National Natural Science Foundation of China[61832002] ; National Natural Science Foundation of China[61532009] ; National Natural Science Foundation of China[62002355] ; National Natural Science Foundation of China[U1705262] ; National Natural Science Foundation of China[U1836220] ; National Natural Science Foundation of China[61702511] ; National Natural Science Foundation of China[61672267] ; National Natural Science Foundation of China[61751211] ; Key Research Program of Frontier Sciences, CAS[QYZDJ-SSW-JSC039] ; National Postdoctoral Program for Innovative Talents[BX20190367]
WOS研究方向	Engineering
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000766700400062
资助机构	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
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/48114]
专题	自动化研究所_模式识别国家重点实验室_多媒体计算与图形学团队
通讯作者	Xu, Changsheng
作者单位	1.Chinese Acad Sci, Natl Lab Pattern Recognit, Inst Automat, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 3.Peng Cheng Lab, Shenzhen 518066, Peoples R China
推荐引用方式 GB/T 7714	Zhang, Xi,Zhang, Feifei,Xu, Changsheng. Joint Expression Synthesis and Representation Learning for Facial Expression Recognition[J]. IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,2022,32(3):1681-1695.
APA	Zhang, Xi,Zhang, Feifei,&Xu, Changsheng.(2022).Joint Expression Synthesis and Representation Learning for Facial Expression Recognition.IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,32(3),1681-1695.
MLA	Zhang, Xi,et al."Joint Expression Synthesis and Representation Learning for Facial Expression Recognition".IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 32.3(2022):1681-1695.