One could imagine building some sort of bag-of-texton-words classifier that can predict whether a face or a car exists inside an image window, and then attempt to fit an AAM model for that object class inside the window (like Nik suggested).

When attempting to use AAMs for cars, it appears that one would need a collection of spatial models (one for each aspect). Faces are somewhat easy, because they are essentially 2D objects that are rich with internal features (the eyes, nose, and mouth are almost always seen in the same spatial configuration) while cars look differently from different points of view.

If one can do what I outline above, then I imagine that AAM-style model fitting can be useful for distinguishing cars from buses or cows from horses (something that bags of words might not be good at). One could probably use one AAM for car/bus like things and another AAM for four legged mammals. Then the distinction between cars and buses or cows and horses could be achieved by building a classifier on top of the AAM parameter space.