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

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.

• Type: Conference
• Year: 2006
• Where: ICPR
• Authors: Michael Reiter, Rene Donner, Georg Langs, Horst Bischof
• Keywords: Canonical Correlation Analysis, Computer Vision, Face Images, Face Texture, ICPR 2006