Most image registration problems are formulated in an asymmetric fashion. Given a pair of images, one is implicitly or explicitly regarded as a template, and warped onto the other to match as well as possible. In this paper, we focus on this seemingly arbitrary choice of the roles, and reveal how it may lead to biased warp estimates in the presence of relative scaling. We present a principled way of selecting the template, and explain why only the correct asymmetric form, with the potential inclusion of a blurring step, can yield an unbiased estimator. We validate our analysis in the domain of model-based face tracking. We show how the usual Active Appearance Model (AAM) formulation overlooks the asymmetry issue, causing the fitting accuracy to degrade quickly when the observed objects are smaller than their model. We formulate a novel, “resolution-aware fitting” (RAF) algorithm that respects the asymmetry, and incorporates an explicit model of the blur caused by the camera’s ...