Modern identification and verification systems typically provide components that capture an image of a person, and then convert the image to some mathematical representation, such as a vector whose components are the intensities of the pixels in the image. The vector, with associated circuitry and hardware, may then be compared with reference vectors of known individuals. A match between the vector and a reference vector can yield the identity of the person.
Such techniques, while straightforward, place an enormous processing burden on the system because the number of pixels in a typical image may be on the order of tens of thousands. Comparing an unknown vector to thousands of reference vectors takes too long. Prior art systems have addressed this problem by using Principal Component Analysis (PCA) on image data to reduce the amount of data that needs to be stored to operate the system efficiently. An example of such a system is set forth in U.S. Pat. No. 5,164,992, the contents of which are hereby incorporated by reference. However, although PCA can decrease the time required for identification to allow for real-time operation, the method can sometimes yield inaccurate results. Such inaccuracies can arise for a variety of reasons, one of which involves problems that arise by not accounting for different types of light that can illuminate an object.