GAGM-AAM: A genetic optimization with Gaussian mixtures for Active Appearance Models

摘要

This paper proposes an optimization technique of genetic algorithm (GA) combined with Gaussian mixtures (GAGM) to make a robust, efficient and real time face alignment application for embedded systems. It uses 2.5D Active Appearance Model (AAM) for the face search, the model is generated by taking 3D landmarks and 2D texture of the face image. 3D face alignment requires to optimize 6DOF (Degrees of Freedom) pose and appearance parameters of AAM. These parameters span in a huge face search space. In order to optimize them GA (due to its exploration property) is taken as an optimization technique, but unfortunately it suffers from massive computations. Thanks to the clustering of appearance parameters by Gaussian Mixture, GA optimization becomes time efficient and accurate. We compare it with other technique of simplex, which is found to be more efficient than classical AAM.