Free The Numbers Behind NUMB3RS by Keith Devlin

nodal points, key features of the face such as the centers of the eyes, the depths of the eye sockets, cheekbones, jaw line, chin, the width of the nose, and the tip of the nose. (See figure 3.) Using fast computers, it is possible to compute the face print of a target individual and compare it to the face prints in a database within a few seconds. The comparison cannot be exact, since the angle of observation of the target will be different from that of each photograph used to generate the face print in the database, although this effect can be overcome in part by means of some elementary trigonometric calculations. But this is the kind of “closest match” comparison task that neural networks can handle well.

    Figure 3. Many facial recognition systems are based on measurements of and between key locations on the face called nodal points.
    One advantage of facial recognition using neural network comparisons of face prints is that it is not affected by surface changes such as wearing a hat, growing or removing a beard, or aging. The first organizations to make extensive use of facial recognition systems were the casinos, who used them to monitor players known to be cheaters. Airport immigration is a more recent, and rapidly growing application of the same technology.
    While present-day facial recognition systems are nowhere near as reliable as they are depicted in movies and in television dramas—particularly in the case of recognizing a face in a crowd, which remains a difficult challenge—the technology is already useful in certain situations, and promises to increase in accuracy over the next few years.
    The reason that facial recognition is of some use in casinos and airport immigration desks is that at those locations the target can be photographed alone, full face on, against a neutral background. But even then, there are difficulties. For example, in 2005, Germany started issuing biometric passports, but problems arose immediately due to people smiling. The German authorities had to issue guidelines warning that people “must have a neutral facial expression and look straight at the camera.”
    On the other hand, there are success stories. On December 25, 2004, the Los Angeles Times reported a police stop west of downtown Los Angeles, where police who were testing a new portable facial recognition system questioned a pair of suspects. One of the officers pointed the system, a hand-held computer with a camera attached, toward one of the two men. Facial recognition software in the device compared the image with those in a database that included photos of recent fugitives, as well as just over a hundred members of two notorious street gangs. Within seconds, the screen had displayed a gallery of nine faces with contours similar to the suspect’s. The computer concluded that one of those images was the closest match, with a 94 percent probability of accuracy.
    THE CASE OF THE SUSPICIOUS CONFERENCE CALLS
    Detecting telephone fraud is another important application of neural networks.
    Dr. Colleen McCue was, for many years, the program manager for the crime analysis unit at the Richmond Police Department in Richmond, Virginia, where she pioneered the use of data-mining techniques in law enforcement. In her book Data Mining and Predictive Analysis , she describes one particular project she worked on that illustrates the many steps that must often be gone through in order to extract useful information from the available data. In this case, a Kohonen neural net was used to identify clusters in the data, but as Dr. McCue explains, there were many other steps in the analysis, most of which had to be done by hand. Just as in regular police detective work, where far more time is spent on routine “slogging” and attention to details than on the more glamorous and exciting parts dramatized in movies and on TV, so too with data mining. Labor-intensive manipulation and preparation of the data by humans