In my last entry I hypothesized that the under-performance of my first detector, when compared to the one documented in the original paper, could be explained by the difference in the number of features used. To verify this, I trained a classifier with only 10K features and run tests to compare both detectors. The results are illustrated in the next graph.
As illustrated by the graphic, the classifier trained with 10K features scores ~4 points bellow the one trained with 15K features at the reference value of 0.0001 FPPW. Having this results in mind, it is safe to assume that if the classifier was trained with 30K features, as it was in the original paper, the detector would most likely achieve results similar to the ones documented in the publication.