What is Face Recognition?

Facial recognition systems are built on computer programs that analyze images of human faces for the purpose of identifying them. The programs take a facial image, measure characteristics such as the distance between the eyes, the length of the nose, and the angle of the jaw, and create a unique file called a "template." Using templates, the software then compares that image with another image and produces a score that measures how similar the images are to each other. Typical sources of images for use in facial recognition include video camera signals and pre-existing photos such as those in driver's license databases.
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 New Orleans 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 New ham has also experimented with the technology.

Why Face Recognition?

Given the requirement for determining people's identity, the obvious question is what technology is best suited to supply this information? There are many different identification technologies available, many of which have been in wide-spread commercial use for years. The most common person verification and identification methods today are Password/PIN (Personal Identification Number) systems, and Token systems (such as your driver's license). Because such systems have trouble with forgery, theft, and lapses in users' memory, there has developed considerable interest in biometric identification systems, which use pattern recognition techniques to identify people using their physiological characteristics. Fingerprints are a classic example of a biometric; newer technologies include retina and iris recognition.
While appropriate for bank transactions and entry into secure areas, such technologies have the disadvantage that they are intrusive both physically and socially. They require the user to position their body relative to the sensor, and then pause for a second to `declare' themselves. This `pause and declare' interaction is unlikely to change because of the fine-grain spatial sensing required. Moreover, there is a `oracle-like' aspect to the interaction: since people can't recognize other people using this sort of data, these types of identification do not have a place in normal human interactions and social structures.
While the `pause and present' interaction and the oracle-like perception are useful in high-security applications (they make the systems look more accurate), they are exactly the opposite of what is required when building a store that recognizes its best customers, or an information kiosk that remembers you, or a house that knows the people who live there.
Face recognition from video and voice recognition have a natural place in these next-generation smart environments -- they are unobtrusive (able to recognize at a distance without requiring a `pause and present' interaction), are usually passive (do not require generating special electro-magnetic illumination), do not restrict user movement, and are now both low-power and inexpensive. Perhaps most important, however, is that humans identify other people by their face and voice, therefore are likely to be comfortable with systems that use face and voice recognition

Facial Recognition Applications

Facial recognition is deployed in large-scale citizen identification applications, surveillance applications, law enforcement applications such as booking stations, and kiosks. It is most often deployed in 1:N environments, searching databases of facial images for close matches. Facial recognition is not as adept at 1:1 verification; facial recognition vendors have attempted to penetrate the desktop login market, but the technology is not optimized for desktop authentication. 

Facial Recognition Market

Facial recognition technology is expected to grow rapidly as customers deploy it for criminal and civil identification applications, including surveillance and screening, through 2007. Increased revenues will be primarily attributable to use in large-scale ID projects in which facial imaging already takes place and the technology can leverage existing processes, such as drivers' licensing, passport issuance applications, and voter registration. In addition, facial recognition technology's use in surveillance applications is expected to increase significantly in public and private sector applications. Because of its unique ability to perform surveillance, as well as the fact that facial images are acquired as part of nearly every document and ID issuance process, facial recognition stands to benefit strongly from post 9/11 deployment decisions. Facial recognition revenues are projected to grow from $34.4m in 2002 to $429.1m in 2007 and are expected to comprise approximately 10% of the entire biometric market.