Light-Invariant Fitting of Active Appearance Models

Abstract

This paper deals with shading and AAMs. Shading is created by lighting change. It can be of two types: self- shading and external shading. The effect of self-shading can be explicitly learned and handled by AAMs. This is not however possible for external shading, which is usually dealt with by robustifying the cost function. We take a different approach: we measure the fitting cost in a so-called Light-Invariant space. This approach naturally handles self-shading and external shading. The framework is based on mild assumptions on the scene reflectance and the cameras. Some photometric camera response parameters are required. We propose to estimate these while fitting an existing color AAM in a photometric 'self-calibration' manner. We report successful results with a face AAM with test images taken indoor under simple lighting change.