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So what then is there about "human"? Everyone took the same courses and more or less familiar with the basic algorithms. The task of recognizing a passport-lik

So what then is there about "human"? Everyone took the same courses and more or less familiar with the basic algorithms. The task of recognizing a passport-like pictures is nearly trivial, while a "random, on the street" face recognition is a different problem. People are using environmental and contextual cues (so, does facebook's system) and they mostly guessing rather than "exact matching" peoples. Anyway, as long as it is not a passport photos the task of face recognition has almost nothing to do with basic NN algorithms, but a lot with contexts and cues.

gcr 12y ago
Exactly. That's why we should be (IMO) focusing on hard face recognition problems, full of rotation and occlusion and blur and all the "icky" parts of the real world that we don't like to deal with. There is some of that work going on out there, but I would be very surprised to find that spirit in a paper with the words "LFW" in the title. It's just a different focus. In its time, LFW was "hard" since most of the face datasets (FERET, AT&T) were completely controlled: the subject visited a laboratory, sat down in the best pose, made the best face expression, and had their picture taken by the best camera that grant funding could buy. The "in the wild" part of "Labeled Faces in the Wild" alludes to the fact that these are more real-world than what the community was used to. It's time to continue that train of thought and move on to something harder though.