Offline Deformable Face Tracking in Arbitrary Videos
File(s)
Published version
Author(s)
Chrysos, G
Antonakos, E
Zafeiriou, S
Snape, P
Type
Conference Paper
Abstract
Generic face detection and facial landmark localization in static imagery are among the most mature and well-studied problems in machine learning and computer vision. Currently, the top performing face detectors achieve a true positive rate of around 75-80% whilst maintaining low false positive rates. Furthermore, the top performing facial landmark localization algorithms obtain low point-to-point errors for more than 70% of commonly benchmarked images captured under unconstrained conditions. The task of facial landmark tracking in videos, however, has attracted much less attention. Generally, a tracking-by-detection framework is applied, where face detection and landmark localization are employed in every frame in order to avoid drifting. Thus, this solution is equivalent to landmark detection in static imagery. Empirically, a straightforward application of such a framework cannot achieve higher performance, on average, than the one reported for static imagery. In this paper, we show for the first time, to the best of our knowledge, that the results of generic face detection and landmark localization can be used to recursively train powerful and accurate person-specific face detectors and landmark localization methods for offline deformable tracking. The proposed pipeline can track landmarks in very challenging long-term sequences captured under arbitrary conditions. The pipeline was used as a semi-automatic tool to annotate the majority of the videos of the 300-VW Challenge.
Date Issued
2015-12-07
Date Acceptance
2015-06-06
Citation
Proceedings of IEEE International Conference on Computer Vision Workshops, 300 Videos in the Wild (300-VW): Facial Landmark Tracking in-the-Wild Challenge & Workshop, 2015, pp.954-962
Publisher
IEEE
Start Page
954
End Page
962
Journal / Book Title
Proceedings of IEEE International Conference on Computer Vision Workshops, 300 Videos in the Wild (300-VW): Facial Landmark Tracking in-the-Wild Challenge & Workshop
Copyright Statement
© 2015 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
Engineering & Physical Science Research Council (EPSRC)
Engineering & Physical Science Research Council (EPSRC)
Grant Number
EP/J017787/1
EP/L026813/1
Source
2015 IEEE International Conference on Computer Vision Workshops
Publication Status
Published
Start Date
2015-12-07
Finish Date
2015-12-13
Coverage Spatial
Santiago, Chile