Adaptive and constrained algorithms for inverse compositional Active Appearance Model fitting

Parametric models of shape and texture such as active appearance models (AAMs) are diverse tools for deformable object appearance modeling and have found important applications in both image synthesis and analysis problems. Among the numerous algorithms that have been proposed for AAM fitting, those based on the inverse-compositional image alignment technique have recently received considerable attention due to their potential for high efficiency. However, existing fitting algorithms perform poorly when used in conjunction with models exhibiting significant appearance variation, such as AAMs trained on multiple-subject human face images. We introduce two enhancements to inverse-compositional AAM matching algorithms in order to overcome this limitation. First, we propose fitting algorithm adaptation, by means of (a) fitting matrix adjustment and (b) AAM mean template update. Second, we show how prior information can be incorporated and constrain the AAM fitting process. The inverse-compositional nature of the algorithm allows efficient implementation of these enhancements. Both techniques substantially improve AAM fitting performance, as demonstrated with experiments on publicly available multi-face datasets.