The Iris Exchange (IREX) III report which evaluated research of iris recognition software from 11 different sources – is said to be the first public and independent evaluation of commercially available algorithms that use iris recognition for finding an individual match within a large database of potential identities.
NIST evaluated 92 different iris recognition algorithms from nine companies and two university labs, all of which submitted software to an open competition held by NIST. 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,” said Patrick Grother of 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.”
Accuracy varied substantially across the algorithms tested. Success rates ranged between 90% and 99% among the algorithms, meaning that no software was perfect, and some produced as many as 10 times more errors than others.
While some algorithms would be fast enough to run through a dataset equivalent to the size of the entire US 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.
