CASIA OpenIR  > 多媒体计算与图形学团队
Bilateral Ordinal Relevance Multi-instance Regression for Facial Action Unit Intensity Estimation
Yong Zhang1,2; Rui Zhao3; Weiming Dong1; Bao-Gang Hu1; Qiang Ji3
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
PublisherIEEE
Abstract

Automatic intensity estimation of facial action units (AUs) is challenging in two aspects. First, capturing subtle changes of facial appearance is quite difficult. Second, the annotation of AU intensity is scarce and expensive. Intensity annotation requires strong domain knowledge thus only experts are qualified. The majority of methods directly apply supervised learning techniques to AU intensity estimation while few methods exploit unlabeled samples to improve the performance. In this paper, we propose a novel weakly supervised regression model-Bilateral Ordinal Relevance Multi-instance Regression (BORMIR), which learns a frame-level intensity estimator with weakly labeled sequences. From a new perspective, we introduce relevance to model sequential data and consider two bag labels for each bag. The AU intensity estimation is formulated as a joint regressor and relevance learning problem. Temporal dynamics of both relevance and AU intensity are leveraged to build connections among labeled and unlabeled image frames to provide weak supervision. We also develop an efficient algorithm for optimization based on the alternating minimization framework. Evaluations on three expression databases demonstrate the effectiveness of the proposed method.

Indexed ByEI
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/23902
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.University of Chinese Academy of Sciences
3.Rensselaer Polytechnic Institute
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Yong Zhang,Rui Zhao,Weiming Dong,et al. Bilateral Ordinal Relevance Multi-instance Regression for Facial Action Unit Intensity Estimation[C]:IEEE,2018.
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