[tt] Algorithms better than humans at face recognition

[Hughes, James J.]

http://www.technologyreview.com/Infotech/18796/

Technology Review - Published by MIT

Wednesday, May 30, 2007

Better Face-Recognition Software
Computers outperform humans at recognizing faces in recent tests.

By Mark Williams

For scientists and engineers involved with face-recognition
technology,the recently released results of the Face Recognition Grand
Challenge--more fully, the Face Recognition Vendor Test (FRVT) 2006 and
the Iris Challenge Evaluation (ICE) 2006--have been a quiet triumph.
Sponsored by the National Institute of Standards and Technology (NIST),
the match up of face-recognition algorithms showed that machine
recognition of human individuals has improved tenfold since 2002 and a
hundredfold since 1995. Indeed, the best face-recognition algorithms now
perform more accurately than most humans can manage. Overall,
facial-recognition technology is advancing rapidly.

Jonathon Phillips, program manager for the NIST tests and lead author of
the agency's report, says that the intended goal of the Face Recognition
Grand Challenge was always an order-of-magnitude improvement in
recognition performance over the results from 2002. Phillips believes
that the necessary decrease in error rate to achieve that goal was due
in large measure to the development of high-resolution still-images and
3-D face-recognition algorithms. "For the FRVT 2006 and the ICE 2006,
sets of high-resolution face images, 3-D face scans, and iris images
were collected of the same people," Phillips says. "The FRVT 2006 for
the first time measured the performance of six 3-D algorithms on a set
of 3-D face scans. The ICE 2006 measured the performance of ten
algorithms on a set of iris images. 3-D face recognition has come into
its own in the last few years because 3-D sensors for face recognition
have become available only recently. What 3-D face recognition
contributes is that it directly captures information about the shapes of
faces."

Among other advantages, 3-D facial recognition identifies individuals by
exploiting distinctive features of a human face's surface--for instance,
the curves of the eye sockets, nose, and chin, which are where tissue
and bone are most apparent and which don't change over time.
Furthermore, Phillips says, "changes in illumination have adversely
affected face-recognition performance from still images. But the shape
of a face isn't affected by changes in illumination." Hence, 3-D face
recognition might even be used in near-dark conditions.

According to Ralph Gross, a researcher at the Carnegie Mellon Robotics
Institute, in Pittsburgh, 3-D facial recognition can also recognize
subjects at different view angles up to 90 degrees--in other words,
faces in profile. "Face recognition has been getting pretty good at full
frontal faces and 20 degrees off, but as soon as you go towards profile,
there've been problems." Gross says that the explanation for
face-recognition software's difficulties with profiles may be no more
complicated than the fact that no one was focusing on the problem. The
main applications of face recognition have been in contexts like ID
cards and face scanners, for which the aim has been recognition of the
full frontal faces of cooperative subjects under controlled lighting.

High-resolution still images have been another factor in the improvement
of face-recognition technology, in part because highly detailed
skin-texture analysis has also become possible. With such analysis, any
patch of skin--called a skin print--can be captured as an image, then
broken up into smaller blocks that algorithms turn into mathematical,
measurable spaces in which lines, pores, and the actual skin texture are
recorded. "It can identify differences between identical twins, which
isn't yet possible using facial-recognition software alone," Gross
explains. "By combining facial recognition with surface-texture
analysis, accurate identification can increase by 20 to 25 percent."

What about the FRVT report's claim that some face-recognition algorithms
equal or exceed humans' recognition capabilities? Phillips explains:
"Humans are very good at recognizing faces of familiar people. However,
they aren't so good at recognizing unfamiliar people." Since many
proposed face-recognition systems would complement or replace humans,
the FRVT's comparative tests of the face-recognition capabilities of
humans and software--the first such testing--were important for
measuring the potential effectiveness of applications. Phillips says
that at low false accept rates (a false accept rate is the measure of
the likelihood that a biometric security system will incorrectly accept
an access attempt by an unauthorized individual), six out of seven
automatic face-recognition algorithms were comparable to or better than
human recognition. These were algorithms from Neven Vision, Viisage,
Cognitec, Identix, Samsung Advanced Institute for Technology, and
Tsinghua University. Unfortunately, Phillips adds, "because the majority
of FRVT 2006 participants haven't disclosed the details of their
methods, it's not possible yet to assess what's distinctive about these
algorithms."

How does the commercial payoff for face recognition look? Quite
promising, because dozens of companies aim to cash in on face
recognition's potential as a biometric for credentialing and
verification purposes. For the FRVT, venerable corporations like Toshiba
and Samsung competed alongside companies like Neven Vision--just
acquired by Google--and Viisage and Identix (which have just merged into
L1 Identity Solutions), as well as alongside researchers from
universities as diverse as Beijing, Cambridge, and Carnegie Mellon. What
applications does a company like Google foresee for the technology
developed by its recent acquisition, Neven Vision? According to a Google
PR person, "We believe it offers promising integration possibilities
with Google's services, such as Picasa and Picasa Web Albums,
particularly in terms of helping users organize and search their own
photos."

At Carnegie Mellon, Ralph Gross says that among other efforts, he and
his colleagues have been "involved with local DMVs in order to scan
images for driver's licenses. I've gotten reports from the state level
to say that, using face-recognition technology, they caught quite a
number of people who applied for licenses in either different states or
in the same state under a different name because their previous license
got suspended." It's a growing trend. States using such technology
include Massachusetts, Illinois, West Virginia, Wisconsin, Colorado,
North and Southern Carolina, Oklahoma, North Dakota, Arkansas, and
Mississippi. Nevertheless, Gross stresses, applying face-recognition
technology to ID photos is a long way from having the capability that
would let law enforcement search a city's webcam networks for specific
individuals. "With driver's license photos, you have a controlled
background, an operator telling you exactly how to position your face;
the images are collected under comparable conditions. It's much more
restricted than the random-face-in-the-crowd problem, where you're
sticking a camera on a building."

Still, Gross says, "you can already see the path building." Until
recently, the video-surveillance industry still mostly relied on analog
cameras, requiring cable to be set up for long distances to connect
those cameras to monitoring equipment. Now, "the industry is switching
to IP-based cameras, with which you can pretty easily tap into already
existing Ethernet networks," Gross says. "So you have wireless cameras
and cameras using POE [Power over Ethernet technology allows IP
telephones, wireless LAN Access Points, and other appliances to receive
power as well as data over existing LAN cabling] where you don't need a
separate power plug. You can buy commercial solutions that are
essentially a TiVo for these cameras, with motion sensors built in so
they only record when there's motion happening. With digital storage,
you can keep the data indefinitely and enhance it in ways that you can't
with analog images. So all these things are coming together."

In principle, therefore, as face-recognition software continues its
rapid advance, it will likely be possible to search for specific faces
across a network of webcams. Accordingly, Gross's recent work at
Carnegie Mellon, in conjunction with colleagues at the Data Privacy Lab
there, has been the development of algorithms to protect individuals'
privacy while under video surveillance. The usual methods that thwart
human recognition of an individual's features on video--for example,
those pixelated fields sometimes covering faces and body parts on
reality-TV shows--already won't fool much face-recognition software.
Completely blacking out each face in a video clip would do the job, but
this would be of limited use if law-enforcement agencies wanted to
follow up evidence of suspicious behavior once they had a court warrant.
The function of the privacy-preserving algorithms that Gross is helping
to create, he explains, is to automatically take the average values of
individuals' faces and, from those, synthesize new facial images, then
superimpose those new images over the originals. "It may seem like the
opposite technology," Gross says, "but actually, it's just the other
side of face recognition."

Copyright Technology Review 2007.
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