Conference Paper 2016

Fitting a 3D morphable model to edges: A comparison between hard and soft correspondences

Edgefitting

In this paper we explore the problem of fitting a 3D morphable model to single face images using only sparse geometric features (edges and landmark points). Previous approaches to this problem are based on nonlinear optimisation of an edge-derived cost that can be viewed as forming soft correspondences between model and image edges. We propose a novel approach, that explicitly computes hard correspondences. The resulting objective function is non-convex but we show that a good initialisation can be obtained efficiently using alternating linear least squares in a manner similar to the iterated closest point algorithm. We present experimental results on both synthetic and real images and show that our approach outperforms methods that use soft correspondence and other recent methods that rely solely on geometric features.

Author(s): Anil Bas and William A. P. Smith and Timo Bolkart and Stefanie Wuhrer
Book Title: ACCV Workshop on Facial Informatics
Pages: 377-391
Year: 2016
Month: June
Bibtex Type: Conference Paper (inproceedings)
DOI: 10.1007/978-3-319-54427-4_28
Event Name: ACCV 2016 International Workshops
Event Place: Taipei, Taiwan
Electronic Archiving: grant_archive
Links:

BibTex

@inproceedings{Bas_ACCVW_2016,
  title = {Fitting a 3D morphable model to edges: A comparison between hard and soft correspondences },
  booktitle = {ACCV Workshop on Facial Informatics},
  abstract = {In this paper we explore the problem of fitting a 3D morphable model to single face images using only sparse geometric features (edges and landmark points). Previous approaches to this problem are based on nonlinear optimisation of an edge-derived cost that can be viewed as forming soft correspondences between model and image edges. We propose a novel approach, that explicitly computes hard correspondences. The resulting objective function is non-convex but we show that a good initialisation can be obtained efficiently using alternating linear least squares in a manner similar to the iterated closest point algorithm. We present experimental results on both synthetic and real images and show that our approach outperforms methods that use soft correspondence and other recent methods that rely solely on geometric features.},
  pages = {377-391},
  month = jun,
  year = {2016},
  slug = {bas_accvw_2016},
  author = {Bas, Anil and Smith, William A. P. and Bolkart, Timo and Wuhrer, Stefanie},
  month_numeric = {6}
}