Shape from shading
A morphable model for the synthesis of 3D faces
Proceedings of the 26th annual conference on Computer graphics and interactive techniques
New Constraints on Data-Closeness and Needle Map Consistency for Shape-from-Shading
IEEE Transactions on Pattern Analysis and Machine Intelligence
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
Face Recognition Based on Fitting a 3D Morphable Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
3D and Infrared Face Reconstruction from RGB data using Canonical Correlation Analysis
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
Recovering Facial Shape Using a Statistical Model of Surface Normal Direction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Facial Shape-from-shading and Recognition Using Principal Geodesic Analysis and Robust Statistics
International Journal of Computer Vision
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
Molding face shapes by example
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
A Coupled Statistical Model for Face Shape Recovery From Brightness Images
IEEE Transactions on Image Processing
Pattern Recognition Letters
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In this paper, we compare four different Subspace Multiple Linear Regression methods for 3D face shape prediction from a single 2D intensity image. This problem is situated within the low observation-to-variable ratio context, where the sample covariance matrix is likely to be singular. Lately, efforts have been directed towards latent-variable based methods to estimate a regression operator while maximizing specific criteria between 2D and 3D face subspaces. Regularization methods, on the other hand, impose a regularizing term on the covariance matrix in order to ensure numerical stability and to improve the out-of-training error. We compare the performance of three latent-variable based and one regularization approach, namely, Principal Component Regression, Partial Least Squares, Canonical Correlation Analysis and Ridge Regression. We analyze the influence of the different latent variables as well as the regularizing parameters in the regression process. Similarly, we identify the strengths and weaknesses of both regularization and latent-variable approaches for the task of 3D face prediction.