Active Appearance Models (AAM) are compact representations of the shape and appearance of objects. Fitting AAMs to images is a difficult, non-linear optimization task. Traditional approaches minimize the L2 norm error between the model instance and the input image warped onto the model coordinate frame. While this works well for high resolution data, the fitting accuracy degrades quickly at lower resolutions. In this paper, we show that a careful design of the fitting criterion can overcome many of the low resolution challenges. In our resolution-aware formulation (RAF), we explicitly account for the finite size sensing elements of digital cameras, and simultaneously model the processes of object appearance variation, geometric deformation, and image formation. As such, our Gauss-Newton gradient descent algorithm not only synthesizes model instances as a function of estimated parameters, but also simulates the formation of low resolution images in a digital camera. We compare the RAF algorithm against a state-of-the-art tracker across a variety of resolution and model complexity levels. Experimental results show that RAF considerably improves the estimation accuracy of both shape and appearance parameters when fitting to low resolution data.
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