We're all a bit worried about the terrifying surveillance state that becomes possible when you cross omnipresent cameras with reliable facial recognition -- but a new study suggests that some of the best algorithms are far from infallible when it comes to sorting through a million or more faces. The University of Washington's MegaFace Challenge is an open competition among public facial recognition algorithms that's been running since late last year. The idea is to see how systems that outperform humans on sets of thousands of images do when the database size is increased by an order of magnitude or two. "We're the first to suggest that face recs algorithms should be tested at'planet-scale,'" wrote the study's lead author, Ira Kemelmacher-Shlizerman, in an email to TechCrunch. "I think that many will agree it's important. The big problem is to create a public dataset and benchmark (where people can compete on the same data). Creating a benchmark is typically a lot of work but a big boost to a research area."