IRIS images may soon be able to do more than just verify your identity - they may confirm your race and gender too.
The iris controls the size of the
pupil and gives a person's eyes their colour. It grows into a complex
and unique pattern as a fetus develops and remains the same throughout a
person's life. This fact has been successfully exploited in iris-based
biometric systems, which work on the principle that each iris is
completely different to any other.
But that is not strictly true, as
Kevin Bowyer at the University of Notre Dame in South Bend, Indiana and
his colleagues have found. They have developed a system that can pick
out similarities between irises, instead of differences. Initial tests
show it can distinguish between people of two different racial
backgrounds and shows promise in determining gender.
"You might assume that there is no similarity in iris texture," says Bowyer, "but you would be wrong."
In a typical iris scan, a camera snaps
an image of a person's eye while it is bathed in near-infrared light.
Software identifies the iris portion of the eye, and then analyses 1024
sample regions, looking for patterns in the way the delicate filaments
of tissue, known as the stroma, reflect light. This unique information
is then used to generate a code of binary numbers.
Bowyer's team's method adds a layer of
complexity. For each of the sample regions, their software identifies
features such as lines or spots in the stroma, and saves that
information. It also records how brightness varies across each region.
This richer set of attributes allowed
the researchers to train an algorithm to look for common features among
irises of known ethnicity and gender. When they turned the system on a
database of unknown irises of 1200 people, it predicted whether a person
was Chinese or Caucasian with over 90 per cent accuracy, and correctly
identified gender 62 per cent of the time. The team will present the
research next month at the IEEE International Conference on Technologies
for Homeland Security in Waltham, Massachusetts.
The reason for the low success rate in
predicting gender, Bowyer says, is because the team have not yet fully
worked out which textural features of the iris correspond to gender. He
says that the fact that the results are better than chance means it
should be possible to improve the system's ability to determine gender.
The team has also not yet tested the system on people with other or more
complex ethnic backgrounds.
Aside from making it difficult for
people to fabricate a false identity in which they have a different
gender or race, the method could speed up searches within large iris
databases by reducing the data subset to be searched. It would also be
possible to count the number of people belonging to different ethnic
backgrounds coming into a country without recording their identity.
"It is interesting work that does fly a
bit in the face of conventional thinking," says Vijayakumar Bhagavatula
of Carnegie Mellon University in Pittsburgh, Pennsylvania. Iris
patterns are generally considered to be highly random; even a person's
left and right iris are different. Still, he says, "in the absence of an
established biological connection between iris pattern and gender or
ethnicity, there is no way to know if the features being used by Bowyer
are the 'best' ones to use. There may be other features that give better
prediction rates."
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