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September 16, 2010 | Airport facial-recognition software to detect stressed-out tourists | Washington: With new advances in facial-recognition software, airport security workers might one day know with near certainty whether they're looking at a stressed-out tourist or staring a terrorist in the eye. A
research team led by Dr. Alice O'Toole, a professor in The University of Texas
at Dallas' School of Behavioural and Brain Sciences, is evaluating how well these
rapidly evolving recognition programs work. The researchers are comparing the
rates of success for the software to the rates for non-technological, but presumably
"expert" human evaluation. "The government is interested in spotting people who
might pose a danger. But they also don't want to have too many false alarms and
detain people who are not real risks," said O'Toole. O'Toole is leading a team
that is examining where facial-recognition algorithms succeed and where they come
up short. The researchers are carefully examining where the algorithms succeed
and where they come up short. They're using point-by-point comparisons to examine
similarities in millions of faces captured within a database, and then comparing
results to algorithm determinations. In the studies, humans and algorithms decided
whether pairs of face images, taken under different illumination conditions, were
pictures of the same person or different people. The UT Dallas researchers have
worked with algorithms that match up still photos and are now moving into
comparisons
involving more challenging images, such as faces caught on video or photographs
taken under poor lighting conditions. "Many of the images that security people
have to work with are not high-quality. They may be taken off closed-circuit
television
or other low-resolution equipment," said O'Toole. The study is likely to continue
through several more phases, as more and better software programs are presented
for review. So far, the results of man vs. machine have been a bit surprising,
said O'Toole. "In fact, the very best algorithms performed better than humans
at identifying faces. Because most security applications rely primarily on human
comparisons up until now, the results are encouraging about the prospect of using
face recognition software in important environments," she said. The real success
comes when the software is combined with human evaluation techniques, said
O'Toole.
By using the software to spot potential high-risk individuals and then combining
the software with the judgment of a person, nearly 100 percent of matching faces
were identified, said O'Toole. Next, using a test that spanned all false-alarm
rates, the researchers compared the algorithms with humans of Caucasian and East
Asian descent matching face identity in an identical stimulus set. In this case,
both algorithms performed better on the Caucasian faces, the "majority" race in
the database. The Caucasian face advantage was far larger for the Western algorithm
than for the East Asian algorithm. Humans showed the standard other-race effect
for these faces, but showed more stable performance than the algorithms over
changes
in the race of the test faces. These findings indicate that state-of-the-art
face-recognition
algorithms, like humans, struggle with "other-race face" recognition, said O'Toole.
The companies that develop the most reliable facial recognition software are likely
to reap big profits down the line. "Casinos have been some of the first users
of face recognition software. They obviously want to be able to spot people who
are counting cards and trying to cheat the casino," said O'Toole. The study will
be published in ACM Transactions on Applied Perception.
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