A framework for joint estimation and guided annotation of facial action unit intensity
Author(s)
Walecki, Robert
Rudovic, Ognjen
Pantic, Maja
Pavlovic, Vladimir
Cohn, Jeffrey F
Type
Conference Paper
Abstract
Manual annotation of facial action units (AUs) is highly tedious and time-consuming. Various methods for automatic coding of AUs have been proposed, however, their performance is still far below of that attained by expert human coders. Several attempts have been made to leverage these methods to reduce the burden of manual coding of AU activations (presence/absence). Nevertheless, this has not been exploited in the context of AU intensity coding, which is a far more difficult task. To this end, we propose an expertdriven probabilistic approach for joint modeling and estimation of AU intensities. Specifically, we introduce a Conditional Random Field model for joint estimation of the AU intensity that updates its predictions in an iterative fashion by relying on expert knowledge of human coders. We show in our experiments on two publicly available datasets of AU intensity (DISFA and FERA2015) that the AU coding process can significantly be facilitated by the proposed approach, allowing human coders to faster make decisions about target AU intensity.
Date Issued
2016-12-19
Date Acceptance
2016-06-26
Citation
Computer Vision and Pattern Recognition Workshops (CVPRW), 2016 IEEE Conference on, 2016, pp.1460-1468
ISSN
2160-7508
Publisher
IEEE
Start Page
1460
End Page
1468
Journal / Book Title
Computer Vision and Pattern Recognition Workshops (CVPRW), 2016 IEEE Conference on
Copyright Statement
© 20 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
Identifier
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000391572100176&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
Grant Number
645094
688835
Source
29th IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
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, NV