|
|
|
|
RI | Publications | Constructing and Fitting Active Appearance Models With Occlusion
|
|
Text only version of this site
Constructing and Fitting Active Appearance Models With Occlusion
R. Gross, I. Matthews, and S. Baker
Proceedings of the IEEE Workshop on Face Processing in Video, June, 2004.
Jump to: Download | Abstract | Notes | Text Reference | BibTeX Reference
| Download [Help] |
Adobe portable document format (pdf) [1008 KB]
Copyright notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.
| Abstract |
Active Appearance Models (AAMs) are generative parametric models that have been successfully used in the past to track faces in video. A variety of video applications are possible, including dynamic pose estimation for real-time user interfaces, lip-reading, and expression recognition. To construct an AAM, a number of training images of faces with a mesh of canonical feature points (usually hand-marked) are needed. All feature points have to be visible in all training images. However, in many scenarios parts of the face may be occluded. Perhaps the most common cause of occlusion is 3D pose variation, which can cause self-occlusion of the face. Furthermore, tracking using standard AAM fitting algorithms often fails in the presence of even small occlusions. In this paper we propose algorithms to construct AAMs from occluded training images and to efficiently track faces in videos containing occlusion. We evaluate our algorithms both quantitatively and qualitatively and show successful real-time face tracking on a number of image sequences containing varying degrees of occlusions.
| Notes |
Sponsor: ONR/DOD
Grant ID: N00014-00-1-0915/N41756-03-C4024
Associated center: VASC
Associated labs/groups: People Image Analysis Consortium, Vision for Safe Driving, and Face Group
Associated projects: AAMs with Occlusion, AAM Fitting Algorithms, and Face Model Building and Fitting
| Text Reference |
R. Gross, I. Matthews, and S. Baker, "Constructing and Fitting Active Appearance Models With Occlusion," Proceedings of the IEEE Workshop on Face Processing in Video, June, 2004.
| BibTeX Reference |
@inproceedings{Gross_2004_4666,
author = "Ralph Gross and Iain Matthews and Simon Baker",
title = "Constructing and Fitting Active Appearance Models With Occlusion",
booktitle = "Proceedings of the IEEE Workshop on Face Processing in Video",
month = "June",
year = "2004"
}