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Learning Representations for Facial Actions From Unlabeled Videos

文献类型：期刊论文

作者Li, Yong2,3; Zeng, Jiabei3; Shan, Shiguang1,2,3
刊名IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
出版日期2022
卷号44期号:1页码:302-317
关键词Facial action unit detection self-supervised learning representation learning feature disentanglement encoder-decoder structure
ISSN号0162-8828
DOI10.1109/TPAMI.2020.3011063
英文摘要Facial actions are usually encoded as anatomy-based action units (AUs), the labelling of which demands expertise and thus is time-consuming and expensive. To alleviate the labelling demand, we propose to leverage the large number of unlabelled videos by proposing a twin-cycle autoencoder (TAE) to learn discriminative representations for facial actions. TAE is inspired by the fact that facial actions are embedded in the pixel-wise displacements between two sequential face images (hereinafter, source and target) in the video. Therefore, learning the representations of facial actions can be achieved by learning the representations of the displacements. However, the displacements induced by facial actions are entangled with those induced by head motions. TAE is thus trained to disentangle the two kinds of movements by evaluating the quality of the synthesized images when either the facial actions or head pose is changed, aiming to reconstruct the target image. Experiments on AU detection show that TAE can achieve accuracy comparable to other existing AU detection methods including some supervised methods, thus validating the discriminant capacity of the representations learned by TAE. TAE's ability in decoupling the action-induced and pose-induced movements is also validated by visualizing the generated images and analyzing the facial image retrieval results qualitatively and quantitatively.
资助项目National Key R&D Program of China[2017YFA0700800] ; National Natural Science Foundation of China[61702481] ; National Natural Science Foundation of China[61976203]
WOS研究方向Computer Science ; Engineering
语种英语
WOS记录号WOS:000728561300022
出版者IEEE COMPUTER SOC
源URL[http://119.78.100.204/handle/2XEOYT63/18044]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Shan, Shiguang
作者单位1.CAS Ctr Excellence Brain Sci & Intelligence Techn, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
3.Chinese Acad Sci, Inst Comp Technol, CAS, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Li, Yong,Zeng, Jiabei,Shan, Shiguang. Learning Representations for Facial Actions From Unlabeled Videos[J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,2022,44(1):302-317.
APA Li, Yong,Zeng, Jiabei,&Shan, Shiguang.(2022).Learning Representations for Facial Actions From Unlabeled Videos.IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,44(1),302-317.
MLA Li, Yong,et al."Learning Representations for Facial Actions From Unlabeled Videos".IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 44.1(2022):302-317.

入库方式： OAI收割

来源：计算技术研究所

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