3D and Infrared Face Reconstruction from RGB data using Canonical Correlation Analysis

Authors:
Michael Reiter;Rene Donner;Georg Langs;Horst Bischof
Affiliations:
Vienna University of Technology, Austria;Graz University of Technology, Austria;Vienna University of Technology, Austria;Graz University of Technology, Austria
Venue:
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
Year:
2006

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Abstract

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.