By Topic

Enhanced 3D geometric-model-based face tracking in low resolution with appearance model

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Zhen Wen ; IBM Thomas J. Watson Res. Center, Yorktown Heights, NY, USA ; Huang, T.S.

3D geometric-model-based face tracking utilizes 3D geometric face model to extract facial motion information from video. It has the advantages of dealing with oblique views of faces and it is robust to changes in people, expressions and lighting. Although most existing 3D model-based methods work well with high resolution faces (e.g. face regions around 200 × 200 pixels), the face image in real environments (e.g. surveillance video) is often in low resolution. In low resolution, the performance of 3D model-based face tracking can be degraded because the underlying low-level image motion estimation is less reliable. In this paper, we present an appearance-based enhancement to 3D geometric-model-based face tracking in low resolution. First, we estimate the motion parameters of the 3D face model. Then we extract the stabilized texture map image of the face model. The appearance variations in the face texture images are caused by the errors in motion estimation or lighting changes. Therefore these appearance variations are used as additional constraints for refining the motion estimations. Experiments show that the proposed enhancement improves the robustness of 3D model-based tracking in low resolution.

Published in:

Image Processing, 2005. ICIP 2005. IEEE International Conference on  (Volume:2 )

Date of Conference:

11-14 Sept. 2005