Photo filters are blunt instruments. The Hefes and Amaros and Kelvins of the world—the algorithms that transform our snapshots into light-washed little works of EmoArt—are extremely un-selective about the modifications they make to our images. "Style transfer," the translation of plain images according to those algorithms, does its translating for an entire image. Which works fine for the typical Instagram shot (of, say, a blackberry gelato, a silkscreened sunset, a babbling brook), but much less fine for the photos that tend to be the most intimate of the images we take: portraits. Our faces, it turns out, are extremely hard to filter—even when you have the app that promises to make you look flawless.
And that's because our minds are extremely attuned to the nuances of other people's appearances. "Our eyes are so sensitive to human faces," YiChang Shih, an MIT graduate student in electrical engineering and computer science, tells PhysOrg. "We're just intolerant to any minor errors." Which means that the filters we use to stylize our pictures may be great for images of nature and baked goods... but they're not great for stylizing each other. Or ourselves.
Shih and his colleagues, however, say they've solved that problem. And they've done so through a technique that localizes algorithmic filters—applying them, like so many topical headache medications, directly to the face. So: you know how portrait photographers like Diane Arbus and Richard Avedon had signature styles—the results of the artists' intentional adjustments of lens and light and shadow? Shih and his colleagues are claiming that they can replicate those styles, essentially, via algorithm.