Within the past few years, that accuracy gap has dramatically narrowed. “In close range, facial recognition systems are almost quite perfect,” says Xiaoming Liu, a computer scientist at Michigan State University in East Lansing. The best algorithms now can reach nearly 99.9 percent accuracy across skin tones, ages and genders.

But high accuracy has a steep cost: individual privacy. Corporations and research institutions have swept up the faces of millions of people from the internet to train facial recognition models, often without their consent. Not only are the data stolen, but this practice also potentially opens doors for identity theft or oversteps in surveillance.

To solve the privacy issues, a surprising proposal is gaining momentum: using synthetic faces to train the algorithms.