Institutional Repository of Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Bilateral Ordinal Relevance Multi-instance Regression for Facial Action Unit Intensity Estimation | |
Yong Zhang1,2![]() ![]() ![]() | |
2018-06 | |
Conference Name | 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) |
Source Publication | IEEE/CVF Conference on Computer Vision and Pattern Recognition |
Conference Date | 2018-6 |
Conference Place | Salt Lake City, Utah |
Publisher | IEEE |
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 By | EI |
Document Type | 会议论文 |
Identifier | http://ir.ia.ac.cn/handle/173211/23902 |
Collection | 模式识别国家重点实验室_三维可视计算 |
Corresponding Author | Qiang Ji |
Affiliation | 1.NLPR, Institute of Automation, Chinese Academy of Sciences 2.University of Chinese Academy of Sciences 3.Rensselaer Polytechnic Institute |
First Author Affilication | Chinese 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. |
Files in This Item: | There are no files associated with this item. |
Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.
Edit Comment