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Joint Expression Synthesis and Representation Learning for Facial Expression Recognition | |
Zhang, Xi1,2![]() ![]() | |
发表期刊 | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
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ISSN | 1051-8215 |
2022-03-01 | |
卷号 | 32期号:3页码:1681-1695 |
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. |
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 |
DOI | 10.1109/TCSVT.2021.3056098 |
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[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] |
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 |
WOS研究方向 | Engineering |
WOS类目 | Engineering, Electrical & Electronic |
WOS记录号 | WOS:000766700400062 |
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 |
国重实验室规划方向分类 | 多模态协同认知 |
是否有论文关联数据集需要存交 | 否 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/48115 |
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, 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 |
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, 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. |
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