How well do facial recognition algorithms cope with a million strangers?

In the last few years, several groups have announced that their facial recognition systems have achieved near-perfect accuracy rates, performing better than humans at picking the same face out of the crowd.

But those tests were performed on a dataset with only 13,000 images—fewer people than attend an average professional U.S. soccer game. What happens to their performance as those crowds grow to the size of a major U.S. city?

University of Washington researchers answered that question with the MegaFace Challenge, the world's first competition aimed at evaluating and improving the performance of face recognition algorithms at the million person scale. All of the algorithms suffered in accuracy when confronted with more distractions, but some fared much better than others.