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Local and Global Perception Generative Adversarial Network for Facial Expression Synthesis

文献类型：期刊论文

作者Xia, Yifan7; Zheng, Wenbo5,6; Wang, Yiming7; Yu, Hui7; Dong, Junyu4; Wang, Fei-Yue1,2,3
刊名IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
出版日期2022-03-01
卷号32期号:3页码:1443-1452
关键词Task analysis Face recognition Mouth Generative adversarial networks Facial features Generators Gallium nitride Facial expression synthesis generative adversarial networks facial expression recognition local facial region facial mask
ISSN号1051-8215
DOI10.1109/TCSVT.2021.3074032
通讯作者Yu, Hui(hui.yu@port.ac.uk)
英文摘要Facial expression synthesis has gained increasing attention with the development of Generative Adversarial Networks (GANs). However, it is still very challenging to generate high-quality facial expressions since the overlapping and blur commonly appear in the generated facial images especially in the regions with rich facial features such as eye and mouth. Generally, existing methods mainly consider the face as a whole in facial expression synthesis without paying specific attention to the characteristics of facial expressions. In fact, according to the physiological and psychological research, the differences of facial expressions often appear in crucial regions such as eye and mouth. Motivated by this observation, a novel end-to-end facial expression synthesis method called Local and Global Perception Generative Adversarial Network (LGP-GAN) with a two-stage cascaded structure is proposed in this paper which is designed to extract and synthesize the details of the crucial facial regions. LGP-GAN can combine the generated results from the global network and local network into the corresponding facial expressions. In Stage I, LGP-GAN utilizes local networks to capture the local texture details of the crucial facial regions and generate local facial regions, which fully explores crucial facial region domain information in facial expressions. And then LGP-GAN uses a global network to learn the whole facial information in Stage II to generate the generate final facial expressions building upon local generated results from Stage I. We conduct qualitative and quantitative experiments on the commonly used public database to verify the effectiveness of the proposed method. Experimental results show the superiority of the proposed method over the state-of-the-art methods.
WOS关键词IMAGE SYNTHESIS ; DEEP
资助项目Engineering and Physical Sciences Research Council (EPSRC)[EP/N025849/1] ; Royal Academy of Engineering[IFS1819/9]
WOS研究方向Engineering
语种英语
WOS记录号WOS:000766700400044
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
资助机构Engineering and Physical Sciences Research Council (EPSRC) ; Royal Academy of Engineering
源URL[http://ir.ia.ac.cn/handle/173211/48108]  
专题自动化研究所_复杂系统管理与控制国家重点实验室_先进控制与自动化团队
通讯作者Yu, Hui
作者单位1.Chinese Acad Sci, State Key Lab Management & Control Complex Syst, Inst Automat, Beijing 100190, Peoples R China
2.Qingdao Acad Intelligent Ind, Qingdao 266109, Peoples R China
3.Macau Univ Sci & Technol, Inst Syst Engn, Macau 999078, Peoples R China
4.Ocean Univ China, Dept Informat Sci & Technol, Qingdao 266100, Peoples R China
5.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
6.Xi An Jiao Tong Univ, Sch Software Engn, Xian 710049, Peoples R China
7.Univ Portsmouth, Sch Creat Technol, Portsmouth PO1 2DJ, Hants, England
推荐引用方式
GB/T 7714		 Xia, Yifan,Zheng, Wenbo,Wang, Yiming,et al. Local and Global Perception Generative Adversarial Network for Facial Expression Synthesis[J]. IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,2022,32(3):1443-1452.
APA Xia, Yifan,Zheng, Wenbo,Wang, Yiming,Yu, Hui,Dong, Junyu,&Wang, Fei-Yue.(2022).Local and Global Perception Generative Adversarial Network for Facial Expression Synthesis.IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,32(3),1443-1452.
MLA Xia, Yifan,et al."Local and Global Perception Generative Adversarial Network for Facial Expression Synthesis".IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 32.3(2022):1443-1452.

入库方式： OAI收割

来源：自动化研究所

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