As for "outperforming humans", a more accurate statement might be, "this algorithm outperforms (for this simplistic task) one experiment done with a limited set of humans on this one particular dataset which has been in the community for 10 years now and is thus highly gameable."
But I realize that's a lot less pithy.
In particular, this dataset is nearing saturation, and whenever that happens, differences in the accuracy numbers often don't mean much. So for example with Facebook's number at 97.53% and this paper's at 98.52%, you're talking about the difference between getting 148 pairs of faces wrong vs 89 pairs wrong. In practical terms, as a researcher working with a dataset like this, you very quickly learn to focus on just the ones your algorithm gets wrong, and it's impossible to not subconsciously try to optimize for getting those few cases correct, even if those techniques wouldn't actually help in the general case.