Unlike other biometric systems,
facial recognition can be used for general surveillance, usually
in combination with public video cameras. There have been three
such uses of face-recognition in the U.S. so far. The first is
in airports, where they have been proposed - and in a few cases
adopted - in the wake of the terrorist attacks of September 11.
Airports that have announced adoption of the technology include
Logan Airport in Boston, T.F. Green Airport in Providence, R.I.,
and San Francisco International Airport and the Fresno Airport
in California.
A second use of the technology was at the 2001 Super Bowl in
Tampa, where pictures were taken of every attendee as they
entered the stadium through the turnstiles and compared against
a database of some undisclosed kind. The authorities would not
say who was in that database, but the software did flag 19
individuals. The police indicated that some of those were false
alarms, and no one flagged by the system was anything more than
a petty criminal such as a ticket scalper. Press reports
indicate that NewOrleans authorities are considering using it
again at the 2002 Super Bowl.
The technology has also been deployed by a part of Tampa, Ybor
City, which has trained cameras on busy public sidewalks in the
hopes of spotting criminals. As with the Super Bowl, it is
unclear what criteria were used for including photos in the
database. The operators have not yet caught any criminals. In
addition, in England, where public, police-operated video
cameras are widespread, the town of Newham has also experimented
with the technology. |
|
Facial recognition utilizes
distinctive features of the face - including the upper outlines
of the eye sockets, the areas surrounding the cheekbones, the
sides of the mouth, and the location of the nose and eyes - to
perform verification and identification. Most technologies are
somewhat resistant to moderate changes in hairstyle, as they do
not utilize areas of the face located near the hairline. When
used in identification mode, facial recognition technology
generally returns candidate lists of close matches as opposed to
returning a single definitive match (as do fingerprint and
iris-scan technologies).
Image Quality
The performance of facial recognition technology is very closely
tied to the quality of the facial image. Low-quality images are
much more likely to result in enrollment and matching errors
than high-quality images. For example, many photograph databases
associated with drivers' licenses or passports contain
photographs of marginal quality, such that importing these files
and executing matches may lead to reduced accuracy. Similarly
well-known problems exist with surveillance deployments. If
facial images for enrollment and matching can be acquired from
live subjects with high-quality equipment, system performance
increases substantially. For facial recognition at slightly
greater-than-normal distances, there is a strong correlation
between camera quality and system capabilities.
Facial Scan Process Flow
As with all biometrics, 4 steps - sample capture, feature
extraction, template comparison, and matching - define the
process flow of facial scan technology. Enrollment generally
consists of a 20-30 second enrollment process whereby several
pictures are taken of one's face. Ideally, the series of
pictures will incorporate slightly different angles and facial
expressions, to allow for more accurate matching. After
enrollment, distinctive features are extracted (or global
reference images are generated), resulting in the creation of a
template. The template is much smaller than the image from which
it is derived: facial images can require 15-30kb, templates
range from 84 bytes to 3000 bytes. The smaller templates are
normally used for 1:N matching.
Verification and identification follow the same steps. Assuming
your audience is a cooperative audience (as opposed to
uncooperative or non-cooperative), the user 'claims' an identity
through a login name or a token, stands or sits in front of the
camera for a few seconds, and is either matched or not matched.
This comparison is based on the similarity of the newly created
match template against the reference template or templates on
file. The point at which two templates are similar enough to
match, known as the threshold, can be adjusted for different
personnel, PC's, time of day, and other factors.
Verification vs. Identification
System design for facial scan verification vs. identification
differ in a number of ways. The primary difference is that
identification does not utilize a claimed identity. Instead of
employing a PIN or user name, then delivering confirmation or
denial of the claim, identification systems attempt to answer
the question "Who am I?" If there are only a handful of
enrollees in the database, this requirement is not demanding; as
databases grow very large, into the tens and hundreds of
thousands, this task becomes much more difficult. The system may
only be able to narrow the database to a number of likely
candidates. Human intervention may then be required at the final
verification stages.
A second variable in identification is the dynamic between the
target subjects and capture device. In verification, one assumes
a cooperative audience, one comprised of subjects who are
motivated to use the system correctly. Facial scan systems,
depending on the exact type of implementation, may also have to
be optimized for non-cooperative and uncooperative subjects.
Non-cooperative subjects are unaware that a biometric system is
in place, or do not care, and make no effort to either be
recognized or to avoid recognition. Uncooperative subjects
actively avoid recognition, and may use disguises or take
evasive measures. Facial scan technologies are much more capable
of identifying cooperative subjects, and are almost entirely
incapable of identifying uncooperative subjects.
Primary Facial Recognition Technologies
The four primary methods employed by facial recognition vendors
to identify and verify subjects include eigenfaces, feature
analysis, neural network, and automatic face processing. Some
types of facial scan technology are more suitable than others
for applications such as forensics, network access, and
surveillance.
"Eigenface," roughly translated as "one's own face," is a
technology patented at MIT which utilizes two dimensional,
global grayscale images representing distinctive characteristics
of a facial image. Variations of eigenface are frequently used
as the basis of other face recognition methods.
|
