Face Reconstruction from Skull Shapes and Physical Attributes

Authors:
Pascal Paysan;Marcel Lüthi;Thomas Albrecht;Anita Lerch;Brian Amberg;Francesco Santini;Thomas Vetter
Affiliations:
Computer Science Department, University of Basel, Switzerland;Computer Science Department, University of Basel, Switzerland;Computer Science Department, University of Basel, Switzerland;Computer Science Department, University of Basel, Switzerland;Computer Science Department, University of Basel, Switzerland;Division of Radiological Physics, University of Basel Hospital, Switzerland;Computer Science Department, University of Basel, Switzerland
Venue:
Proceedings of the 31st DAGM Symposium on Pattern Recognition
Year:
2009

Citing 3
Cited 1

Reanimating the dead: reconstruction of expressive faces from skull data

ACM SIGGRAPH 2003 Papers
Kernel Methods for Pattern Analysis

Kernel Methods for Pattern Analysis
Curvature guided level set registration using adaptive finite elements

Proceedings of the 29th DAGM conference on Pattern recognition

A hierarchical dense deformable model for 3D face reconstruction from skull

Multimedia Tools and Applications

Quantified Score

Hi-index	0.00

Visualization

Abstract

Reconstructing a person's face from its skeletal remains is a task that has over many decades fascinated artist and scientist alike. In this paper we treat facial reconstruction as a machine learning problem. We use separate statistical shape models to represent the skull and face morphology. We learn the relationship between the parameters of the models by fitting them to a set of MR images of the head and using ridge regression on the resulting model parameters. Since the facial shape is not uniquely defined by the skull shape, we allow to specify target attributes, such as age or weight. Our experiments show that the reconstruction results are generally close to the original face, and that by specifying the right attributes the perceptual and measured difference between the original and the predicted face is reduced.