3D face reconstruction under imperfect tracking circumstances using shape model constraints

Authors:
H. Fang;N. P. Costen
Affiliations:
Manchester Metropolitan University, Department of Computing and Mathematics, Manchester, UK;Manchester Metropolitan University, Department of Computing and Mathematics, Manchester, UK
Venue:
ISVC'07 Proceedings of the 3rd international conference on Advances in visual computing - Volume Part II
Year:
2007

Citing 9
Cited 0

Shape and motion from image streams under orthography: a factorization method

International Journal of Computer Vision
A robust technique for matching two uncalibrated images through the recovery of the unknown epipolar geometry

Artificial Intelligence - Special volume on computer vision
A morphable model for the synthesis of 3D faces

Proceedings of the 26th annual conference on Computer graphics and interactive techniques
Active Appearance Models

IEEE Transactions on Pattern Analysis and Machine Intelligence
Matching 2.5D Face Scans to 3D Models

IEEE Transactions on Pattern Analysis and Machine Intelligence
A Closed-Form Solution to Non-Rigid Shape and Motion Recovery

International Journal of Computer Vision
Generic vs. person specific active appearance models

Image and Vision Computing
Real-time combined 2D+3D active appearance models

CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Sparse models for gender classification

FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition

Quantified Score

Hi-index	0.00

Visualization

Abstract

We propose a factorization structure from motion (SfM) framework which employs 3D active shape constraints for a 3D face model application. Two types of shape model, individual shape models and a generic model, are used to approximate non-linear manifold variation. When the 3D shape models are trained, they help the SfM algorithm to reconstruct the 3D face structure under noisy observation (tracking) circumstances. By minimizing two sets of errors, the reconstruction error generated by the linear transform of the shape models and projection error obtained by re-projecting the 3D shape to 2D positions, the 3D face shapes can be recovered optimally. Experimental results show that this algorithm accurately reconstructs the 3D shape of familiar and non-familiar faces from video sequences under circumstances of imperfect face tracking or noisy observations.