Real Time Feature Based 3-D Deformable Face Tracking
Refereed conference paper presented and published in conference proceedings


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AbstractIn this paper, we develop a novel framework for 3D tracking non-rigid face deformation from a single camera. The difficulty of the problem lies in the fact that 3D deformation parameter estimation becomes unstable when there are few reliable facial features correspondences. Unfortunately, this often occurs in real tracking scenario when there is significant illumination change, motion blur or large pose variation. In order to extract more information of feature correspondences, the proposed framework integrates three types of features which discriminate face deformation across different views: 1) the semantic features which provide constant correspondences between 3D model points and major facial features; 2) the silhouette features which provide dynamic correspondences between 3D model points and facial silhouette under varying views; 3) the online tracking features that provide redundant correspondences between 3D model points and salient image features. The integration of these complementary features is important for robust estimation of the 3D parameters. In order to estimate the high dimensional 3D deformation parameters, we develop a hierarchical parameter estimation algorithm to robustly estimate both rigid and non-rigid 3D parameters. We show the importance of both features fusion and hierarchical parameter estimation for reliable tracking 3D face deformation. Experiments demonstrate the robustness and accuracy of the proposed algorithm especially in the cases of agile head motion,
All Author(s) ListZhang W, Wang Q, Tang XO
Name of Conference10th European Conference on Computer Vision (ECCV 2008)
Start Date of Conference12/10/2008
End Date of Conference18/10/2008
Place of ConferenceMarseille
Country/Region of ConferenceFrance
Journal nameLecture Notes in Artificial Intelligence
Year2008
Month1
Day1
Volume Number5303
PublisherSPRINGER-VERLAG BERLIN
Pages720 - 732
ISBN978-3-540-88685-3
ISSN0302-9743
LanguagesEnglish-United Kingdom
Web of Science Subject CategoriesComputer Science; Computer Science, Artificial Intelligence; Computer Science, Software Engineering; Computer Science, Theory & Methods; Imaging Science & Photographic Technology

Last updated on 2021-23-01 at 00:36