Face Recognition

A system based on local feature analysis uses a camera and computer to identify a person in a crowd. First it scans a field of view for shapes that could be faces. It then searches for facial features like those already stored in its memory. To be sure the eyes, nose and mouth belong to a living being-and not a mannequin-the program looks for eye-blinks or other telltale facial movements. The system then analyzes the pixels that make up the face image. It compares the darkness of each pixel to that of its neighbors, looking for areas where abrupt differences in value radiate outward from a single pixel. These changes can occur between the eyebrows and skin, the eyes and eyelids, or on features that protrude, such as the cheekbones and nose. The system plots the location of each pixel, known as an “anchor point,” then connects the dots, forming a mesh of triangles. It measures the angles of each triangle and comes up with a number made of 672 ones and zeroes that identifies the face. The program attempts to match that number to a similar one in its database. There can never be a perfect match, so the program ranks how confident it is about the identification. And since the program plots the anchor points by bone structure, disguises such as beards, makeup and eyeglasses won’t fool it.

Like feature analysis, the eigenface method also reduces a face to a number. But instead of looking at a collection of facial features locally, it examines the face as a whole. First it averages out a database of head shots to produce one composite face. Then it compares the face being identified to the composite. An algorithm measures how much the target face differs from the composite and generates a 128-digit personal identification number based on the deviation. Both systems offer security at the expense of constant surveillance. Whether society is willing to pay that cost is yet to be determined.