For a complete list of publications of the Robotics and Advanced Manufacturing group, see: Publications RYMA
2009
Artículos de revista
Castelan, Mario; Van Horebeek, Johan
Relating intensities with three-dimensional facial shape using partial least squares Artículo de revista
En: IET Computer Vision, vol. 3, no 2, pp. 60-73, 2009, ISSN: 1751-9632.
@article{5069092,
title = {Relating intensities with three-dimensional facial shape using partial least squares},
author = {Castelan, Mario and Van Horebeek, Johan},
url = {http://ieeexplore.ieee.org/document/4563049/?arnumber=4563049},
doi = {10.1049/iet-cvi.2008.0060},
issn = {1751-9632},
year = {2009},
date = {2009-06-01},
journal = {IET Computer Vision},
volume = {3},
number = {2},
pages = {60-73},
abstract = {The authors apply partial least squares regression to predict three-dimensional (3D) face shape from a single image. PLS describes the relationship between independent (intensity images) and dependent (3D shape) variables by seeking directions in the space of independent variables that are associated with large variations in the space of dependent variables. We use this idea to construct statistical models of intensity and 3D shape that capture strongly linked variations in both spaces. This decomposition leads to the construction of two different models that capture common variations in 3D shape and intensity. Using the intensity model, a set of parameters is obtained from out-of-training intensity examples. These intensity parameters can then be used directly in the 3D shape model to approximate facial shape. Experiments show that prediction is achieved with reasonable accuracy, improving results obtained through canonical correlation analysis.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The authors apply partial least squares regression to predict three-dimensional (3D) face shape from a single image. PLS describes the relationship between independent (intensity images) and dependent (3D shape) variables by seeking directions in the space of independent variables that are associated with large variations in the space of dependent variables. We use this idea to construct statistical models of intensity and 3D shape that capture strongly linked variations in both spaces. This decomposition leads to the construction of two different models that capture common variations in 3D shape and intensity. Using the intensity model, a set of parameters is obtained from out-of-training intensity examples. These intensity parameters can then be used directly in the 3D shape model to approximate facial shape. Experiments show that prediction is achieved with reasonable accuracy, improving results obtained through canonical correlation analysis.
