In this paper we present a method for online 3D face reconstruction from a video sequence. The face landmarks in a given frame are detected and used to create a 3D shape estimate. The resulting 3D shape is an approximate, sparse representation of the subject’s face. Our reconstruction step is based on a revised version of incremental Structure From Motion, where we use a novel 4D subspace tracking procedure followed by scaled deflation against a vector of ones. Facial landmark detection is built upon a regressor cascade scheme where each subsequent regressor updates the initial shape obtained from the preceding frame.
In this paper we present an evaluation of the chosen versions of Active Appearance Models (AAM) in varying background conditions. Algorithms were tested on a subset of the CMU PIE database and chosen background im- ages. Our experiments prove, that the accuracy of those methods is strictly correlated with the used background, where the differences in the success rate differ even up to 50%.