People are really good at face recognition in real-life, because you have not just a static 2-d view of a person completely out-of-context, but a fully dynamic 3-d view of someone you've probably seen before and you know the lighting environment you're in, meaning you can easily factor out those effects.

A computer algorithm operating on single 2d images (like LFW) has none of that. If you were to ask people to do the same task with the same data, they'd probably still do pretty well (much better than computers), but perhaps not perfectly.

The differences between image-to-image (same person) and different people is exactly what makes this problem so tough in the general case. It was shown about 20 years ago now that faces span a fairly low-dimensional manifold, and across different parts of this manifold, faces of different people do look much more similar than faces of the same person.

I probably shouldn't have explicitly written that formula for recognition, since it's not actually the formula, but more a general scaling rule-of-thumb. But even if we take it as given (hypothetically), there would be several issues. First, remember that 97.53% is on this particular dataset, which is very special in many ways (e.g., it was all collected over the course of a year from photos on Yahoo News, of public figures, with relatively lower-resolution images, and often very distinctive backgrounds). On a more realistic dataset, these numbers would be much lower.

Second, having more images of a person does help, but not nearly as much as you'd hope, because now there's an even bigger chance that you might accidentally match against someone else who happened to have their photo taken in the same pose, lighting conditions, and facial expression as your test photo, and it's very tough for algorithms to discount those confounding factors.

As for other applications of face recognition, let me describe one broad area that I think is pretty exciting, rather than a bunch of specific instances. One of the big shifts in user interface design is going to be "personalization" (to varying degrees). If a program or device or robot can recognize who you are, it can pro-actively change its settings/behavior/performance/etc. to better suit you. A very simple example is the new Kinect, which does some sort of recognition to load your saved profile/controller preferences. But that's just the tip of the iceberg.

And then of course there's personal photo collection management. If your photo organizer knew who everyone was in all your photos, it would be immensely useful in a number of ways. For starters, you could easily search for "photos of me & and my sister" or "my family", etc. You could also do more advanced analysis to start to get at more subtle things. For example, "my high school debate trip" might not immediately seem like it's related to face recognition, but in fact recognition might get you 90% of the way there.

Finally, if you've made it this far, I might as well plug my recent paper, "Photo Recall" [1], which looks at how to do advanced searches on your photo collection. Our system doesn't currently handle faces, but if you look at the kinds of queries we can do, it should become clear how they might extend with faces.

[1] http://homes.cs.washington.edu/~neeraj/projects/photo-recall...