the results were obtained with a relatively general purpose learning algorithm. No extraction of SIFT features, no "hough circle transform to find eyes and noses".

This deserves even more emphasis. All of the other teams were writing tons of domain specific code to implement fancy feature detectors that are the results of years of in-depth research and the subject of many PhDs. The machine learning only comes into play after the manually-coded feature detectors have preprocessed the data.

Meanwhile, the SuperVision team fed raw RGB pixel data directly into their machine learning system and got a much better result.