A morphable model for the synthesis of 3D faces
Proceedings of the 26th annual conference on Computer graphics and interactive techniques
A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms
International Journal of Computer Vision
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
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
Age-Invariant Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
3D Face Reconstruction from a Single Image Using a Single Reference Face Shape
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Coupled Statistical Model for Face Shape Recovery From Brightness Images
IEEE Transactions on Image Processing
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This paper addresses the problem of linearly approximating 3D shape from intensities in the context of facial analysis. In other words, given a frontal pose grayscale input face, the direct estimation of its 3D structure is sought through a regression matrix. Approaches falling into this category generally assume that both 2D and 3D features are defined under Cartesian schemes, which is not optimal for the task of novel view synthesis. The current article aims to overcome this issue by exploiting the 3D structure of faces through cylindrical coordinates, aided by the partial least squares regression. In the context of facial shape analysis, partial least squares builds a set of basis faces, for both grayscale and 3D shape spaces, seeking for maximizing shared covariance between projections of the data along the basis faces. Experimental tests show how the cylindrical representations are suitable for the purposes of linear regression, resulting in a benefit for the generation of novel facial views, showing a potential use in model based face identification.