Copula Ordinal Regression for Joint Estimation of Facial Action Unit Intensity
File(s)copula_ordinal_regression__cvpr2016_final-2.pdf (2.23 MB)
Accepted version
Author(s)
Pantic, M
Rudovic, O
Walecki, R
Pavlovic, V
Type
Conference Paper
Abstract
Joint modeling of the intensity of facial action units (AUs) from face images is challenging due to the large number of AUs (30+) and their intensity levels (6). This is in part due to the lack of suitable models that can efficiently handle such a large number of outputs/classes simultaneously, but also due to the lack of labelled target data. For this reason, majority of the methods proposed so far resort to independent classifiers for the AU intensity. This is suboptimal for at least two reasons: the facial appearance of some AUs changes depending on the intensity of other AUs, and some AUs co-occur more often than others. Encoding this is expected to improve the estimation of target AU intensities, especially in the case of noisy image features, head-pose variations and imbalanced training data. To this end, we introduce a novel modeling framework, Copula Ordinal Regression (COR), that leverages the power of copula functions and CRFs, to detangle the probabilistic modeling of AU dependencies from the marginal modeling of the AU intensity. Consequently, the COR model achieves the joint learning and inference of intensities of multiple AUs, while being computationally tractable. We show on two challenging datasets of naturalistic facial expressions that the proposed approach consistently outperforms (i) independent modeling of AU intensities, and (ii) the state-ofthe-art approach for the target task.
Date Issued
2016-12-12
Date Acceptance
2016-02-29
ISSN
1063-6919
Publisher
IEEE
Journal / Book Title
Proceedings of IEEE Int’l Conf. on Computer Vision and Pattern Recognition (CVPR 2016)
Copyright Statement
© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Sponsor
Commission of the European Communities
Commission of the European Communities
Grant Number
645094
688835
Source
IEEE Int’l Conf. on Computer Vision and Pattern Recognition (CVPR 2016)
Subjects
Science & Technology
Technology
Computer Science, Artificial Intelligence
Computer Science
Publication Status
Published
Start Date
2016-06-26
Finish Date
2016-07-01
Coverage Spatial
Las Vegas, Nevada, USA