Identifying people by acquiring pictures of their eyes is becoming easier, according to a new report on on iris recognition software by the National Institute of Standards and Technology (NIST).

from 11 different organizations and found that some techniques produced very rapid results — though this speed was often at the cost of accuracy.

The NIST Iris Exchange (IREX) III report is the first public, independent comparison of commercially available algorithms that use iris recognition for the challenging task of finding an individual match within a large database of potential identities.

Previous published studies only used single algorithms or considered “one-to-one” verification, in which an individual claims an identity and the software then attempts to confirm whether the claim matches a specific record.

NIST evaluated 92 different iris recognition algorithms from nine private companies and two university labs.. The task was to identify individuals from a database of eye images taken from more than 2.2 million people.

“If, for example, you are trying to pick out a fugitive who is trying to cross a national border, you need to know your software can identify that person from among millions of records,” says Patrick Grother, a scientist in NIST’s Information Access Division. “This ability to pick out a ‘needle in a haystack’ quickly and accurately is crucial, and we found some algorithms can search a haystack thousands of times larger than others. This is important because often there is no corresponding record, no needle to be found.”

Among the results: Accuracy varied substantially across the algorithms the NIST team tested. Success rates ranged between 90 and 99 percent among the algorithms; some produced as many as 10 times more errors than others. Also, the tests found that while some algorithms would be fast enough to run through a dataset equivalent to the size of the entire U.S. population in less than 10 seconds using a typical computer, there could be limitations to their accuracy. A related NIST report showed that accuracy could be improved if operators control image collection more tightly during acquisition, thereby obtaining better quality iris images.