Ensure that we're not already below required leaf false alarm rate before continuing to get negative samples and evaluate entire frame with detectSingleScale.

It seems as though if the existing stages of a classifier were perfect, then fillPassedSamples would either hang forever or run out of negatives to draw on and fail, despite the fact that a perfect classifier has a leaf false alarm rate of zero.
using &lt;= here is wrong; 1) you potentially access memory outside of an image, 2) when the detected face is on the tile boundary, you will output the same face twice (or even 4 times).