Generic facial encoding for shape alignment with active models

Abstract

The modelisation of human faces from images can be done by the mean of morphable models such as AAMs. However, fitting such models without previous estimations is a challenging task. Shape estimation needs a close texture reference, and texture approximation requires shape knowledge. In this paper, we address the efficiency of sampling and generic encoding in regard to the shape alignment accuracy, without previous texture approximation. The hybrid method we propose is based on a relative barycentric resampling of the face model, a generic coding of the reference texture and a normalized cost function. We also present a new warping function definition to simplify the initial global parameter estimation. These new subsampling and encoding frameworks improve the accuracy of facial shape alignment in unconstrained cases. © Springer-Verlag Berlin Heidelberg 2006.