Facial recognition software is a powerful technology that poses serious threats to civil liberties. It’s also a booming business. Today, dozens of startups and tech giants are selling face recognition services to hotels, retail stores—even schools and summer camps. The business is flourishing thanks to new algorithms that can identify people with far more precision than even five years ago. In order to improve these algorithms, companies trained them on billions of faces—often without asking anyone’s permission. Indeed, chances are good that your own face is part of a “training set” used by a facial recognition firm or part of a company’s customer database.
Consumers may be surprised at some of the tactics companies have used to harvest their faces. In at least three cases, for instance, firms have obtained millions of images by harvesting them via photo apps on people’s phones. For now, there are few legal restrictions on facial recognition software, meaning there is little people can do to stop companies using their face in this manner.
In 2018, a camera collected the faces of passengers as they hurried down an airport jetway near Washington, D.C. In reality, neither the jetway nor the passengers were real; the entire structure was merely a set for the National Institute for Science and Technology (NIST) to demonstrate how it could collect faces “in the wild.” The faces would become part of a recurring NIST competition that invites companies across the globe to test their facial recognition software.
In the jetway exercise, volunteers gave the agency consent to use their faces. This is how it worked in the early days of facial recognition; academic researchers took pains to get permission to include faces in their data sets. Today, companies are at the forefront of facial recognition, and they’re unlikely to ask for explicit consent to use someone’s face—if they bother with permission at all.
The companies, including industry leaders like Face++ and Kairos, are competing in a market for facial recognition software that is growing by 20% each year and is expected to be worth $9 billion a year by 2022, according to Market Research Future. Their business model involves licensing software to a growing body of customers—from law enforcement to retailers to high schools—which use it run facial recognition programs of their own.
In the race to produce the best software, the winners will be companies whose algorithms can identify faces with a high degree of accuracy without producing so-called false positives. As in other areas of artificial intelligence, creating the best facial recognition algorithm means amassing a big collection of data—faces, in this case—as a training tool. While companies are able to use the sanctioned collections compiled by government and universities, such as the Yale Face Database, these training sets are relatively small and contain no more than a few thousand faces.
These official data sets have other limitations. Many lack racial diversity or fail to depict conditions—such as shadows or hats or make-up—that can change how faces appear in the real world. In order to build facial recognition technology capable of spotting individuals “in the wild,” companies needed more images. Lots more.
“Hundreds are not enough, thousands are not enough. You need millions of images. If you don’t train the database with people with glasses or people of color, you won’t get accurate results,” says Peter Trepp, the CEO of FaceFirst, a California-based facial recognition company that helps retailers screen for criminals entering their stores.
Where might a company obtain millions of images to train its software? One source has been databases of police mug shots, which are publicly available from state agencies and are also for sale by private companies. California-based Vigilant Solutions, for instance, offers a collection of 15 million faces as part of its facial recognition “solution.”
Some startups, however, have found an even better source of faces: personal photo album apps. These apps, which compile photos stored on a person’s phone, typically contain multiple images of the same person in a wide variety of poses and situations—a rich source of training data.
“We have consumers who tag the same person in thousands of different scenarios. Standing in the shadows, with hats-on, you name it,” says Doug Aley, the CEO of Ever AI, a San Francisco facial recognition startup that launched in 2012 as EverRoll, an app to help consumers manage their bulging photo collections.
Read the source article in Fortune.