CASIA OpenIR  > 多媒体计算与图形学团队
Classifier Learning with Prior Probabilities for Facial Action Unit Recognition
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 units (AUs) play an important role in human emotion understanding. One big challenge for data-driven AU recognition approaches is the lack of enough AU annotations, since AU annotation requires strong domain expertise. To alleviate this issue, we propose a knowledge-driven method for jointly learning multiple AU classifiers without any AU annotation by leveraging prior probabilities on AUs, including expression-independent and expression-dependent AU probabilities. These prior probabilities are drawn from facial anatomy and emotion studies, and are independent of datasets. We incorporate the prior probabilities on AUs as the constraints into the objective function of multiple AU classifiers, and develop an efficient learning algorithm to solve the formulated problem. Experimental results on five benchmark expression databases demonstrate the effectiveness of the proposed method, especially its generalization ability, and the power of the prior probabilities.

Indexed ByEI
Language英语
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/23903
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. Classifier Learning with Prior Probabilities for Facial Action Unit Recognition[C],2018.
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