In a word: excellent.
Training on as little as a single instance of an image even works. It yields a detector with an obviously very specific discriminative selection power (scale/orientation), but it still works for that case.
With a modest number (16, 256, or 1024) of randomly synthesized training images (scale/rotation), the discriminative power still remains to pick out the object of interest, but it works over a larger range of scales and orientations. When I tried translation the classifier started generating much less distinct results but I think that might have been a bug in my synthesising code.
The only problem is that the scale of the detector output changes depending on the range of orientations (not so much the number of training images). Although even with a poorly specified classifier (very many high values in the output), the global peak value is still a reliable indicator of a single instance of the object (which is the most important thing, so long as it is present).
I have only been trying it with still images so far, the next thing to try will be with video. And with other objects than a face (faces are particularly 'interesting' in the LBP space, so it might not work on general objects).