Twenty years ago the problem of face recognition was considered among the hardest in Artificial Intelligence (AI) and computer vision. Surprisingly, however, over the last decade there have been a series of successes that have made the general person identification enterprise appear not only technically feasible but also economically practical.
The apparent tractability of face recognition problem combined with the dream of smart environments has produced a huge surge of interest from both funding agencies and from researchers themselves. It has also spawned several thriving commercial enterprises. There are now several companies that sell commercial face recognition software that is capable of high-accuracy recognition with databases of over 1,000 people.
These early successes came from the combination of well-established pattern recognition techniques with a fairly sophisticated understanding of the image generation process. In addition, researchers realized that they could capitalize on regularities that are peculiar to people, for instance, that human skin colors lie on a one-dimensional manifold (with color variation primarily due to melanin concentration), and that human facial geometry is limited and essentially 2-D when people are looking toward the camera. Today, researchers are working on relaxing some of the constraints of existing face recognition algorithms to achieve robustness under changes in lighting, aging, rotation-in-depth, expression and appearance (beard, glasses, makeup) -- problems that have partial solution at the moment.