Facial Recognition

Wired's fascinating and detailed history of Woody Bledsoe, the creator of facial recognition code, started 60 years ago.

Woody’s facial-recognition research in the 1960s prefigured all these technological breakthroughs and their queasy ethical implications. And yet his early, foundational work on the subject is almost entirely unknown. Much of it was never made public.

Fortunately, whatever Woody’s intentions may have been that day in 1995, the bulk of his research and correspondence appears to have survived the blaze in his garage. Thousands of pages of his papers—39 boxes’ worth—reside at the Briscoe Center for American History at the University of Texas. Those boxes contain, among other things, dozens of photographs of people’s faces, some of them marked up with strange mathematical notations—as if their human subjects were afflicted with some kind of geometrical skin disease. In those portraits, you can discern the origin story of a technology that would only grow more fraught, more powerful, and more ubiquitous in the decades to come.

Woody Bledsoe

Origins ...

Over the following year, Woody came to believe that the most promising path to automated facial recognition was one that reduced a face to a set of relationships between its major landmarks: eyes, ears, nose, eyebrows, lips. The system that he imagined was similar to one that Alphonse Bertillon, the French criminologist who invented the modern mug shot, had pioneered in 1879. Bertillon described people on the basis of 11 physical measurements, including the length of the left foot and the length from the elbow to the end of the middle finger. The idea was that, if you took enough measurements, every person was unique. Although the system was labor-intensive, it worked: In 1897, years before fingerprinting became widespread, French gendarmes used it to identify the serial killer Joseph Vacher.

Endgame ...

Woody and Hart began with a database of around 800 images—two newsprint-quality photos each of about “400 adult male caucasians,” varying in age and head rotation. (I did not see images of women or people of color, or references to them, in any of Woody’s facial-recognition studies.) Using the RAND tablet, they recorded 46 coordinates per photo, including five on each ear, seven on the nose, and four on each eyebrow. Building on Woody’s earlier experience at normalizing variations in images, they used a mathematical equation to rotate each head into a forward-looking position. Then, to account for differences in scale, they enlarged or reduced each image to a standard size, with the distance between the pupils as their anchor metric.

The computer’s task was to memorize one version of each face and use it to identify the other. Woody and Hart offered the machine one of two shortcuts. With the first, known as group matching, the computer would divide the face into features—left eyebrow, right ear, and so on—and compare the relative distances between them. The second approach relied on Bayesian decision theory; it used 22 measurements to make an educated guess about the whole.

In the end, the two programs handled the task about equally well. More important, they blew their human competitors out of the water. When Woody and Hart asked three people to cross-match subsets of 100 faces, even the fastest one took six hours to finish. The CDC 3800 computer completed a similar task in about three minutes, reaching a hundredfold reduction in time. The humans were better at coping with head rotation and poor photographic quality, Woody and Hart acknowledged, but the computer was “vastly superior” at tolerating the differences caused by aging. Overall, they concluded, the machine “dominates” or “very nearly dominates” the humans.

Disquieting is it not? Read the piece, fascinating to a fault without question.