loading...
3D and Infrared Face Reconstruction from RGB data using Canonical Correlation Analysis
Hong Kong August 20-August 24
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICPR.2006.2418th International Conference on Patt ...
 This Article 
 
Buy PURCHASE ARTICLE: $19
PDF
HTML
Buy IEEE Xplore Subscribers
 
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
ASCII Text
x 
 
Michael Reiter, Rene Donner, Georg Langs, Horst Bischof, "3D and Infrared Face Reconstruction from RGB data using Canonical Correlation Analysis," Pattern Recognition, International Conference on, vol. 1, pp. 425-428, 18th International Conference on Pattern Recognition (ICPR'06) Volume 1, 2006.
 
 
BibTex
x
 
@article{ 10.1109/ICPR.2006.24,
author = {Michael Reiter and Rene Donner and Georg Langs and Horst Bischof},
title = {3D and Infrared Face Reconstruction from RGB data using Canonical Correlation Analysis},
journal ={Pattern Recognition, International Conference on},
volume = {1},
year = {2006},
issn = {1051-4651},
pages = {425-428},
doi = {http://doi.ieeecomputersociety.org/10.1109/ICPR.2006.24},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - CONF
JO - Pattern Recognition, International Conference on
TI - 3D and Infrared Face Reconstruction from RGB data using Canonical Correlation Analysis
SN - 1051-4651
SP425
EP428
A1 - Michael Reiter,
A1 - Rene Donner,
A1 - Georg Langs,
A1 - Horst Bischof,
PY - 2006
KW - null
VL - 1
JA - Pattern Recognition, International Conference on
ER -
 
Michael Reiter, Vienna University of Technology, Austria
Rene Donner, Graz University of Technology, Austria
Georg Langs, Vienna University of Technology, Austria
Horst Bischof, Graz University of Technology, Austria
In this paper, we apply a multiple regression method based on Canonical Correlation Analysis (CCA) to face data modelling. CCA is a factor analysis method which exploits the correlation between two high dimensional signals. We first use CCA to perform 3D face reconstruction and in a separate application we predict near-infrared (NIR) face texture. In both cases, the input data are color (RGB) face images. Experiments show, that due to the correlation between input and output signal, only a small number of canonical factors are needed to describe the functional relation of RGB images to the respective output (NIR images and 3D depth maps) with reasonable accuracy.
Citation:
Michael Reiter, Rene Donner, Georg Langs, Horst Bischof, "3D and Infrared Face Reconstruction from RGB data using Canonical Correlation Analysis," icpr, vol. 1, pp.425-428, 18th International Conference on Pattern Recognition (ICPR'06) Volume 1, 2006
Usage of this product signifies your acceptance of the Terms of Use.