ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part I
Using Subspace Multiple Linear Regression for 3D Face Shape Prediction from a Single Image
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part II
A new statistical model combining shape and spherical harmonics illumination for face reconstruction
ISVC'07 Proceedings of the 3rd international conference on Advances in visual computing - Volume Part I
Real-time face tracking and recognition by sparse eigentracker with associative mapping to 3D shape
Image and Vision Computing
Rapid 3D face reconstruction by fusion of SFS and Local Morphable Model
Journal of Visual Communication and Image Representation
Pattern Recognition Letters
Model-Based human teeth shape recovery from a single optical image with unknown illumination
MCV'12 Proceedings of the Second international conference on Medical Computer Vision: recognition techniques and applications in medical imaging
3D shape regression for real-time facial animation
ACM Transactions on Graphics (TOG) - SIGGRAPH 2013 Conference Proceedings
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We focus on the problem of developing a coupled statistical model that can be used to recover facial shape from brightness images of faces. We study three alternative representations for facial shape. These are the surface height function, the surface gradient, and a Fourier basis representation. We jointly capture variations in intensity and the surface shape representations using a coupled statistical model. The model is constructed by performing principal components analysis on sets of parameters describing the contents of the intensity images and the facial shape representations. By fitting the coupled model to intensity data, facial shape is implicitly recovered from the shape parameters. Experiments show that the coupled model is able to generate accurate shape from out-of-training-sample intensity images