A novel convergence scheme for active appearance models

The active appearance model (AAM) algorithm is a powerful tool for modeling images of deformable objects. AAM combines a subspace-based deformable model of an object´s appearance with a fast and robust method of fitting this model to a previously unseen image. The speed of this algorithm comes from the assumption that the gradient matrix is fixed around the optimal coefficients for all images. In this paper, we propose a convergence scheme for AAM that adapts this gradient matrix to the target image´s texture during convergence by adding linear modes of change that are based on the texture eigenvectors of AAM. We show that this adaptive strategy for the gradient matrix provides a significant increase in the performance of the AAM algorithm.