Distance Maps: a Robust Illumination Preprocessing for Active Appearance Models

Methods of deformable appearance models are useful for realistically modelling shapes and textures of visual objects for reconstruction. A first application can be the fine analysis of face gestures and expressions from videos, as deformable appearance models make it possible to automatically and robustly locate several points of interest in face images. That opens development prospects of technologies in many applications like video coding of faces for videophony, animation of synthetic faces, word visual recognition, expressions and emotions analysis, tracking and recognition of faces. However, these methods are not very robust to variations in the illumination conditions, which are expectable in non constrained conditions. This article describes a robust preprocessing method designed to enhance the performances of deformable models methods in the case of lighting variations. The proposed preprocessing is applied to the Active Appearance Models (AAM). More precisely, the contribution consists in replacing texture images (pixels) by distance maps as input of the deformable appearance models methods. The distance maps are images containing information about the distance between edges in the original object images, which enhance the robustness of the AAMs models against lighting variations.