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to my desktop computer, this algorithm found a variety of real, pho- tographed, and hand-drawn faces within a second of their appearance in the field of view, as long as the face was seen full on.The system could also track a face as it moved, if the movement was not too rapid.

The next step after detection, face recognition, is also reaching ma- turity, driven by pressing needs for identification and verification. In identification, an unknown face is compared to a dataset of known faces, such as a security watchlist; in verification, the claimants face is compared to a stored image of the person he or she claims to be. Like face detection, recognition is susceptible to a variety of approaches, such as one developed by the MIT Media Labs Alexander Pentland, who categorizes faces based on a set of visual building blocks he has developed; for instance, the appearance of the upper lip and the fore- head. Computer software uses these fundamental elements to identify faces, with sufficient success that Pentlands method has earned the trust of banks and security agencies.

A recent series of tests of computerized face recognition systems that was sponsored by the FBI, the Secret Service, and other govern- ment agencies, proved that commercially available algorithms had sig- nificantly improved in just two years.Automatic verification software approved 90 percent of legitimate subjects and only 1 percent of im- posters, and an unknown face was correctly identified as belonging to a base set of more than 37,000 faces, with virtual certainty or very high probability, more than 80 percent of the time.

Despite this impressive performance, the government tests showed that there are still kinks. Success rates dropped substantially when the subject was seen under some types of lighting. The rate of correct identification has also been low for faces not seen full on, but this problem has recently been largely alleviated by the morphable model,in which the software generates a three-dimensional model of what the camera sees.This virtual face is then changed and rotated to show how the subject would look if facing forward, and the result is fed into the face recognition routine. In one example, a poor iden- tification rate of 15 percent for subjects looking right or left jumped to 77 percent when the morphable model was employed. This im- provement suggests that better software, coupled with increased com-

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