Learning Mixture Models for Gender Classification Based on Facial Surface Normals
IbPRIA '07 Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part I
Face recognition with irregular region spin images
SCIA'07 Proceedings of the 15th Scandinavian conference on Image analysis
Example-based face shape recovery using the zenith angle of the surface normal
MICAI'07 Proceedings of the artificial intelligence 6th Mexican international conference on Advances in artificial intelligence
Weighted principal geodesic analysis for facial gender classification
CIARP'07 Proceedings of the Congress on pattern recognition 12th Iberoamerican conference on Progress in pattern recognition, image analysis and applications
Gender classification using principal geodesic analysis and gaussian mixture models
CIARP'06 Proceedings of the 11th Iberoamerican conference on Progress in Pattern Recognition, Image Analysis and Applications
Face recognition with region division and spin images
CIARP'06 Proceedings of the 11th Iberoamerican conference on Progress in Pattern Recognition, Image Analysis and Applications
SSPR'06/SPR'06 Proceedings of the 2006 joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Molding face shapes by example
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Rapid 3D face reconstruction by fusion of SFS and Local Morphable Model
Journal of Visual Communication and Image Representation
| Hi-index | 0.00 |
This paper describes how facial shape can be modelled using a statistical model that captures variations in surface normal direction. To construct this model we make use of the azimuthal equidistant projection to map surface normals from the unit sphere to points on a local tangent plane. The variations in surface normal direction are captured using the covariance matrix for the projected point positions. This allows us to model variations in face shape using a standard point distribution model. We train the model on fields of surface normals extracted from range data and show how to fit the model to intensity data using constraints on the surface normal direction provided by Lambert驴s law. We demonstrate that this process yields accurate facial shape recovery and allows an estimate of the albedo map to be made from single, real world face images.