In some sense, the 97% is indeed the more fair number to compare against, assuming that this paper also restricts the algorithms to only see tight crops of the

In some sense, the 97% is indeed the more fair number to compare against, assuming that this paper also restricts the algorithms to only see tight crops of the face. Obviously using more than just tight crops will give you more information, but our point in differentiating those cases when measuring human performance was that the way LFW was constructed gives you MUCH more information when using loose crops than "normal" images would. For example, many images of actors are at award shows (the Oscars in particular), and so if you see that kind of background in a pair of images, you can just say "same" and have a very good chance of getting it right. That's what the "inverse crop" experiment shows [1] -- when you block out the face in LFW images, you can still get 94% accuracy! In normal images, however, the background won't normally give you so much information. I do feel somewhat bad that our human verification performance experiment numbers are now being used to create linkbait titles like, "computer algorithms can beat humans," because that's obviously not true (nor have I ever believed it), but in my defense, in 2009 we didn't really think about what the press would do in 2014 when algorithms started saturating on LFW =) [1] http://homes.cs.washington.edu/~neeraj/projects/faceverification/ http://homes.cs.washington.edu/~neeraj/projects/faceverifica...