Empirical Inference Conference Paper 2008

Automatic 3D Face Reconstruction from Single Images or Video

This paper presents a fully automated algorithm for reconstructing a textured 3D model of a face from a single photograph or a raw video stream. The algorithm is based on a combination of Support Vector Machines (SVMs) and a Morphable Model of 3D faces. After SVM face detection, individual facial features are detected using a novel regression- and classification-based approach, and probabilistically plausible configurations of features are selected to produce a list of candidates for several facial feature positions. In the next step, the configurations of feature points are evaluated using a novel criterion that is based on a Morphable Model and a combination of linear projections. To make the algorithm robust with respect to head orientation, this process is iterated while the estimate of pose is refined. Finally, the feature points initialize a model-fitting procedure of the Morphable Model. The result is a highresolution 3D surface model.

Author(s): Breuer, P. and Kim, KI. and Kienzle, W. and Schölkopf, B. and Blanz, V.
Book Title: FG 2008
Journal: Proceedings of the 8th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2008)
Pages: 1-8
Year: 2008
Month: September
Day: 0
Publisher: IEEE Computer Society
Bibtex Type: Conference Paper (inproceedings)
Address: Los Alamitos, CA, USA
DOI: 10.1109/AFGR.2008.4813339
Event Name: 8th IEEE International Conference on Automatic Face and Gesture Recognition
Event Place: Amsterdam, Netherlands
Digital: 0
Electronic Archiving: grant_archive
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik
Links:

BibTex

@inproceedings{5237,
  title = {Automatic 3D Face Reconstruction from Single Images or Video},
  journal = {Proceedings of the 8th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2008)},
  booktitle = {FG 2008},
  abstract = {This paper presents a fully automated algorithm for reconstructing
  a textured 3D model of a face from a single
  photograph or a raw video stream. The algorithm is based
  on a combination of Support Vector Machines (SVMs) and
  a Morphable Model of 3D faces. After SVM face detection,
  individual facial features are detected using a novel
  regression- and classification-based approach, and probabilistically
  plausible configurations of features are selected
  to produce a list of candidates for several facial feature positions.
  In the next step, the configurations of feature points
  are evaluated using a novel criterion that is based on a
  Morphable Model and a combination of linear projections.
  To make the algorithm robust with respect to head orientation,
  this process is iterated while the estimate of pose is
  refined. Finally, the feature points initialize a model-fitting
  procedure of the Morphable Model. The result is a highresolution
  3D surface model.},
  pages = {1-8},
  publisher = {IEEE Computer Society},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Los Alamitos, CA, USA},
  month = sep,
  year = {2008},
  slug = {5237},
  author = {Breuer, P. and Kim, KI. and Kienzle, W. and Sch{\"o}lkopf, B. and Blanz, V.},
  month_numeric = {9}
}