Knowledge Commons of Institute of Automation,CAS
Joint Representation and Estimator Learning for Facial Action Unit Intensity Estimation | |
Yong Zhang1; Baoyuan Wu1; Weiming Dong2; Zhifeng Li1; Wei Liu1; Bao-Gang Hu2; Qiang Ji3 | |
2019-06 | |
会议名称 | 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) |
会议录名称 | IEEE/CVF Conference on Computer Vision and Pattern Recognition |
页码 | 3457-3466 |
会议日期 | 2019-6 |
会议地点 | Long Beach, CA, USA |
摘要 | Facial action unit (AU) intensity is an index to characterize human expressions. Accurate AU intensity estimation depends on three major elements: image representation, intensity estimator, and supervisory information. Most existing methods learn intensity estimator with fixed image representation, and rely on the availability of fully annotated supervisory information. In this paper, a novel general framework for AU intensity estimation is presented, which differs from traditional estimation methods in two aspects. First, rather than keeping image representation fixed, it simultaneously learns representation and intensity estimator to achieve an optimal solution. Second, it allows incorporating weak supervisory training signal from human knowledge (e.g. feature smoothness, label smoothness, label ranking, and positive label), which makes our model trainable even fully annotated information is not available. More specifically, human knowledge is represented as either soft or hard constraints which are encoded as regularization terms or equality/inequality constraints, respectively. On top of our novel framework, we additionally propose an efficient algorithm for optimization based on Alternating Direction Method of Multipliers (ADMM). Evaluations on two benchmark databases show that our method outperforms competing methods under different ratios of AU intensity annotations, especially for small ratios. |
收录类别 | EI |
语种 | 英语 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/23907 |
专题 | 多模态人工智能系统全国重点实验室_三维可视计算 |
通讯作者 | Baoyuan Wu |
作者单位 | 1.Tencent AI Lab 2.National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences 3.Rensselaer Polytechnic Institute |
推荐引用方式 GB/T 7714 | Yong Zhang,Baoyuan Wu,Weiming Dong,et al. Joint Representation and Estimator Learning for Facial Action Unit Intensity Estimation[C],2019:3457-3466. |
条目包含的文件 | 条目无相关文件。 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论