Given a set of training data I trained a classifier, and then used that to partition the training data - very good true positives or negatives were removed. I then repeated this 3 more times. The final negative data set consists of just 52 images that are very eye-like in the LBP domain: bits of mouth and under the nose. I started with about 4000 negative images and 1000 positive images.
For a fitness score I was maximising the number of false positives with a score better than the worst true positive. This created an ROC curve that rises relatively slowly to start with but keeps rising to a full-pass rate at a fairly low FPR (false positive rate). This isn't normally the ROC curve one seeks because it tends to include plenty of false positives but since i'm seeking to partition the data for a separate stage i'm after the lowest threshold that achieves a TPR of 1 and rejects as much as possible, not the highest TPR vs FPR.
Anyway - it was quite successful, and by 4 stages it had essentially created a perfect classifier to partition that data-set. Each stage tests 4 groups of 4x4 pixels so it only requires a fairly modest amount of work and a very small amount of memory to describe the entire classifier (9*4*4*4 = 576 bytes total). Being a cascade it only processes all stages a small fraction of the time as well.
Of course this is a little too specific to be useful and the training data-sets also need some tweaking - pose and minor scale variation for the positive set and more problematic images for the negative set. So more setup work and experimentation is still required but i'm fairly sure it'll still work on a more general problem.
I got a bit messy with the code and added some big bugs which caused no end of frustration. One I think had been there from the start in certain code-paths - I was showing the LBP image in a window and as part of that it was converting the code to 8 bits not only for display but also for the stored codes. Lets just say this breaks the algorithm although not nearly as much as I would have expected. As part of the OpenCL version I was ordering the scores differently - based on integers and using bitfields to keep track of other info, and I inadvertently added back the bug which interferes with the sort order. Both of these combined to causing the reported success rate to keep climing even though the classifier was just getting worse - what was more confusing is that over time it tended to increase in quality and then start to decline - but only after about 1/2 an hour of processing.