Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
A morphable model for the synthesis of 3D faces
Proceedings of the 26th annual conference on Computer graphics and interactive techniques
Multiple view geometry in computer vision
Multiple view geometry in computer vision
Symmetric Shape-from-Shading Using Self-ratio Image
International Journal of Computer Vision
Model-Based 3D Face Capture with Shape-from-Silhouettes
AMFG '03 Proceedings of the IEEE International Workshop on Analysis and Modeling of Faces and Gestures
A Statistical Method for Robust 3D Surface Reconstruction from Sparse Data
3DPVT '04 Proceedings of the 3D Data Processing, Visualization, and Transmission, 2nd International Symposium
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Combining PCA and LFA for Surface Reconstruction from a Sparse Set of Control Points
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
A 3D Face Model for Pose and Illumination Invariant Face Recognition
AVSS '09 Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance
Efficient 3D reconstruction for face recognition
Pattern Recognition
Driving 3D morphable models using shading cues
Pattern Recognition
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3D face shape provides a pose and illumination invariant description of human faces. In this paper, we propose a novel component based method to recover the full 3D face shape from a set of sparse feature points. We use a local linear fitting (LLF) scheme so that reconstruction of each subregion depends on both its own vertices and adjacent subregions. This method results in a separate set of shape coefficients each emphasizing the quality of one subregion and improves the model expressiveness. Experiments show that the LLF strategy significantly reduces the model residual error, and thus reduces the sparse reconstruction error under pose variations. Moreover, the problem of estimating pose parameters is revisited, and we use a joint optimization method to improve the reconstruction quality under unknown pose. We evaluate the sensitivity of our method to the selection of feature points. Simulation results show that our method is more robust than prevailing methods.