Our brains are incredibly agile machines, and it,s hard to think of anything they do more efficiently than recognize faces. Just hours after birth, the eyes of newborns are drawn to facelike patterns. An adult brain knows it’s seeing a face within 100 milliseconds, and it takes just over a second to realize that two different pictures of a face, even if they’re lit or rotated in very different ways, belong to the same person. Neuroscientists now believe that there may be a specific region of the brain, on the fusiform gyrus of the temporal lobe, dedicated to facial recognition.

Perhaps the most vivid illustration of our gift for recognition is the magic of caricature—the fact that the sparest cartoon of a familiar face, even a single line dashed off in two seconds, can be identified by our brains in an instant. It’s often said that a good caricature looks more like a person than the person himself. As it happens, this notion, counterintuitive though it may sound, is actually supported by research. In the field of vision science, there’s even a term for this seeming paradox—the caricature effect—a phrase that hints at how our brains misperceive faces as much as perceive them.

Human faces are all built pretty much the same: two eyes above a nose that’s above a mouth, the features varying from person to person generally by mere millimeters. So what our brains look for, according to vision scientists, are the outlying features—those characteristics that deviate most from the ideal face we carry around in our heads, the running average of every visage we’ve ever seen. We code each new face we encounter not in absolute terms but in the several ways it differs markedly from the mean. In other words, to beat what vision scientists call the homogeneity problem, we accentuate what’s most important for recognition and largely ignore what isn’t. Our perception fixates on the upturned nose, rendering it more porcine, the sunken eyes or the fleshy cheeks, making them loom larger. To better identify and remember people, we turn them into caricatures.

Ten years ago, the science of facial recognition—until then a somewhat esoteric backwater of artificial-intelligence research—suddenly became a matter of national security. The hazy closed-circuit images of Mohamed Atta, taped breezing through an airport checkpoint in Portland, Maine, enraged Americans and galvanized policymakers to fund research into automated recognition systems. We all imagined that within a few years, as soon as surveillance cameras had been equipped with the appropriate software, each face in a crowd would stand out like a thumbprint, its unique features and configuration offering a biometric key that could be immediately checked against any database of suspects.

But now a decade has passed, and face-recognition systems still perform miserably in real-world conditions. It’s true that in our digital photo libraries, and now on Facebook, pictures of the same person can be automatically tagged and collated with some accuracy. Indeed, in a recent test of face-recognition software sponsored by the National Institute of Standards and Technology, the best algorithms could identify faces more accurately than humans do—at least in controlled settings, in which the subjects look directly at a high-resolution camera, with no big smiles or other displays of feature-altering emotion. To crack the problem of real-time recognition, however, computers would have to recognize faces as they actually appear on video: at varying distances, in bad lighting, and in an ever-changing array of expressions and perspectives. Human eyes can easily compensate for these conditions, but our algorithms remain flummoxed.

Given current technology, the prospects for picking out future Mohamed Attas in a crowd are hardly brighter than they were on 9/11. In 2007, recognition programs tested by the German federal police couldn’t identify eight of 10 suspects. Just this February, a couple that accidentally swapped passports at the airport in Manchester, England, sailed through electronic gates that were supposed to match their faces to file photos.

All this leads science to a funny question. What if, to secure our airports and national landmarks, we need to learn more about caricature? After all, it’s the skill of the caricaturist—the uncanny ability to quickly distill faces down to their most salient features—that our computers most desperately need to acquire. Better cameras and faster computers won’t be enough. To pick terrorists out of a crowd, our bots might need to go to art school—or at least spend some time at the local amusement park.

In the 19th century, law enforcement knew that exaggerated art could catch crooks. When New York’s Boss Tweed, on the lam in Spain, was finally arrested in 1876, he was identified not with the aid of a police sketch but with a Thomas Nast caricature from Harper’s Weekly. Today, though, most police departments use automated facial-likeness generators, which tend to create a bland, average face rather than a recognizable portrait of the guilty party. Paul Wright, the president of Identi-Kit, one of the most commonly used composite systems in the US, concedes that the main value of his product is in ruling out a large fraction of the population. “Half the people might say a composite sketch looks like Rodney Dangerfield, another half like Bill Clinton. But it’s not useless. It doesn’t look like Jack Nicholson.”

Visit the annual convention of the International Society of Caricature Artists and you’ll find people who describe their face-depiction skills in far less modest terms. Take Stephen Silver, who began his career 20 years ago as a caricaturist at Sea World and is now a character designer for TV animation studios. “If they used caricatures for police composites today,” Silver says, “people would be like, ‘What is this, a joke?’ But the cops would catch the guy. If I drew a caricature, the guy would be shit out of luck.”

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