CASIA OpenIR  > 多媒体计算与图形学团队
Weakly-supervised Deep Convolutional Neural Network Learning for Facial Action Unit Intensity Estimation
Yong Zhang; Weiming Dong; Bao-Gang Hu; Qiang Ji
2018-06
Conference Name2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Source PublicationIEEE/CVF Conference on Computer Vision and Pattern Recognition
Conference Date2018-6
Conference PlaceSalt Lake City, Utah
Abstract
Facial action unit (AU) intensity estimation plays an important role in affective computing and human-computer interaction. Recent works have introduced deep neural networks for AU intensity estimation, but they require a large amount of intensity annotations. AU annotation needs strong domain expertise and it is expensive to construct a large database to learn deep models. We propose a novel knowledge-based semi-supervised deep convolutional neural network for AU intensity estimation with extremely limited AU annotations. Only the intensity annotations of peak and valley frames in training sequences are needed. To provide additional supervision for model learning, we exploit naturally existing constraints on AUs, including relative appearance similarity, temporal intensity ordering, facial symmetry, and contrastive appearance difference. Experimental evaluations are performed on two public benchmark databases. With around 2% of intensity annotations in FERA 2015 and around 1% in DISFA for training, our method can achieve comparable or even better performance than the state-of-the-art methods which use 100% of intensity annotations in the training set.
Indexed ByEI
Language英语
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/23904
Collection多媒体计算与图形学团队
Corresponding AuthorQiang Ji
Affiliation1.NLPR, Institute of Automation, Chinese Academy of Sciences
2.Rensselaer Polytechnic Institute
3.University of Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Yong Zhang,Weiming Dong,Bao-Gang Hu,et al. Weakly-supervised Deep Convolutional Neural Network Learning for Facial Action Unit Intensity Estimation[C],2018.
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