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Semi-Supervised Deep Neural Network for Joint Intensity Estimation of Multiple Facial Action Units

文献类型：期刊论文

作者Zhang, Yong2; Fan, Yanbo2; Dong, Weiming3; Hu, Bao-Gang3; Ji, Qiang1
刊名IEEE ACCESS
出版日期2019
卷号7页码:150743-150756
关键词Gold Estimation Hidden Markov models Training Face Task analysis Neural networks Facial action units intensity estimation deep learning weakly supervised learning
ISSN号2169-3536
DOI10.1109/ACCESS.2019.2947201
通讯作者Zhang, Yong(zhangyong201303@gmail.com)
英文摘要Facial action units (AUs) are defined to depict movements of facial muscles, which are basic elements to encode facial expressions. Automatic AU intensity estimation is an important task in affective computing. Previous works leverage the representation power of deep neural networks (DNNs) to improve the performance of intensity estimation. However, a large number of intensity annotations are required to train DNNs that contain millions of parameters. But it is expensive and difficult to build a large-scale database with AU intensity annotation since AU annotation requires annotators have strong domain expertise. We propose a novel semi-supervised deep convolutional network that leverages extremely limited AU annotations for AU intensity estimation. It requires only intensity annotations of keyframes of training sequences. Domain knowledge on AUs is leveraged to provide weak supervisory information, including relative appearance similarity, temporal intensity ordering, facial symmetry, and contrastive appearance difference. We also propose a strategy to train a model for joint intensity estimation of multiple AUs under the setting of semi-supervised learning, which greatly improves the efficiency during inference. We perform empirical experiments on two public benchmark expression databases and make comparisons with state-of-the-art methods to demonstrate the effectiveness of the proposed method.
WOS关键词TRACKING ; MODEL
资助项目National Key Research and Development Program of China[2018YFC0807500] ; National Natural Science Foundation of China (NSFC)[61720106006] ; U.S National Science Foundation Award CNS[1629856]
WOS研究方向Computer Science ; Engineering ; Telecommunications
语种英语
WOS记录号WOS:000497163000031
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
资助机构National Key Research and Development Program of China ; National Natural Science Foundation of China (NSFC) ; U.S National Science Foundation Award CNS
源URL[http://ir.ia.ac.cn/handle/173211/29413]  
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Zhang, Yong
作者单位1.Rensselaer Polytech Inst, Dept Elect Comp & Syst Engn, Troy, NY 12180 USA
2.Tencent AI Lab, Shenzhen 518057, Guangdong, Peoples R China
3.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Zhang, Yong,Fan, Yanbo,Dong, Weiming,et al. Semi-Supervised Deep Neural Network for Joint Intensity Estimation of Multiple Facial Action Units[J]. IEEE ACCESS,2019,7:150743-150756.
APA Zhang, Yong,Fan, Yanbo,Dong, Weiming,Hu, Bao-Gang,&Ji, Qiang.(2019).Semi-Supervised Deep Neural Network for Joint Intensity Estimation of Multiple Facial Action Units.IEEE ACCESS,7,150743-150756.
MLA Zhang, Yong,et al."Semi-Supervised Deep Neural Network for Joint Intensity Estimation of Multiple Facial Action Units".IEEE ACCESS 7(2019):150743-150756.

入库方式： OAI收割

来源：自动化研究所

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