Linear combinations of eigenfaces — images like the above — are the cheapest way to store and search photos of faces. Like if you want to computer analyse the faces of everyone at the Superbowl and see if there’s a terrorist.

All of the terrorists’ faces are saved in a database as like {11% Eigenface_1, 6% Eigenface_2, 1% Eigenface_3, …}. So the real face breaks down to just a list of percentages {11%, 6%, 1%, …}.

Articles on Eigenfaces: 1, 2, 3, 4, 5, 6  (sorry to reinforce the google hierarchy)

If you break the faces in the live Superbowl crowd CCTV’s into such a list of percentages, then you can have a computer do a search against known terrorists. (Imagine having the computer search pixel by pixel. It would be like, Hey, the top left of your head looks like the top left of a terrorist’s head! Whoops, that was just the soda machine.)

Final point. There is more than one way to mathematicise a face. Surface normal vectors are one way; the manifold limit of polygons is another; projection is another; and eigenfaces are another. Each way has you conceive of a face differently.