For over a year, I worked as a beauty editor, writing and researching about the products, trends, and people that make us want to look a certain way. And as research for many of the stories I wrote, I consulted with dermatologists, plastic surgeons, makeup artists, aestheticians, and more trying to answer a simple question—how can I make myself more conventionally attractive?
“Beauty is confidence,” they’d always say, prefacing the real answer. Inevitably, these experts would eventually tell me that you feel more confident, and thus more beautiful, when you look blemish- and wrinkle-free. (Pending on the product they were promoting, this could also incorporate being tanner, or more contoured, or thinner, or paler, or less made up, or curvier, etc.) Regardless of respondents’ different aesthetic tastes, everyone seemed to agree—younger is more beautiful. Beauty was about anti-aging.
Naturally, the problem here is the premise. What is beauty beyond someone else defining it? For as long as humanity’s obsession with the term has existed, we’ve equally known about its subjective nature. After all, “beauty is in the eye of the beholder” is merely a cliché that posits that exact subjectivity of attractiveness.
But what if the beholder can eliminate subjectivity—what if the beholder wasn’t a person, but an algorithm? Using machine learning to define beauty could, theoretically, make beauty pageants and rankings like People’s annual Most Beautiful in the World list more objective and less prone to human error. Of course, teaching an algorithm to do anything may involve some bias from whoever does the programming, but that hasn’t stopped this automated approach from defining equally subjective things like listening preferences or news value (we see you, Facebook et al).
“We don’t want human opinion,” says biotechnologist Dr. Alex Zhavoronkov, one of the founders behind a pageant-holding, beauty-quantifying initiative called Beauty.AI. “At the end of the day, there are lots of disagreements. We’re looking at ways to evaluate beauty, and some ways may be more relevant or less relevant to human perception. But the entire purpose of Beauty.AI is to get rid of human opinion, to transcend it.”
Beauty.AI was merely one of the latest attempts to have technology objectively evaluate beauty. But as an online competition that crowdsourced headshots and allowed bot-driven algorithms to determine rankings, perhaps it represents the fever point of this exercise. If so, the initiative’s outcome made one thing definitively clear: artificial intelligence will never determine a universal face of beauty. Even today, it only highlights how precisely narrow one’s definition of beauty can be.
Before Hot Or Not: A brief history of quantifying beauty
Long before anyone knew what an algorithm was, humanity has attempted to quantify and measure beauty. Leonardo da Vinci’s pen-and-ink drawing of the Vitruvian Man, whose head was one-eighth of its body, was based on Roman architect Vitruvius’ writing on the subject from his treatise, De Architectura. Plato believed that beauty resided in parts that harmoniously fit into the whole. St. Augustine believed that the more geometrically equal something was, the more beautiful it was. The theories went on and on.
And for as long as people have made these landmark statements on beauty, they’ve also revealed obvious cultural bias about their standards of beauty. Northern Renaissance painter Albrecht Dürer used his own fingers, known for being longer than average, to construct a canon of the human body. Or, for a recent example, morning show Good Day DC anchors Wisdom Martin and Maureen Umeh went viral last year for giving the side eye to a 2014 cosmetic surgery studystating that Kate Middleton had the “most desirable face.” Naturally, the study was based on a test group of “normal-appearing white women aged 18 to 25 years.”
In the past few decades, scholars have at least come to accept that universal beauty is a complicated, perhaps impossible thing. One of the more popular works furthering that idea comes from author Naomi Wolf and her 1991 bestselling book, The Beauty Myth. “Beauty is a currency system like the gold standard,” she wrote. “Like any economy, it is determined by politics, and in the modern age in the West it is the last, best belief system that keeps male dominance intact.” Wolf believed that beauty is a construction of capitalism meant to preserve the status quo in the ever-expanding West—essentially arguing that modern, more diverse supermodels like Naomi Campbell and Tyra Banks still had to fit into a rigid definition of beauty that entails things like “tall,” “thin,” and “youthful.”
These cultural complications haven’t stopped modern researchers from looking to tech for a better solution, however. Case in point: University of California, Irvine researchers Natalie A. Popenko and Dr. Brian J. Wong. (Wong, a plastic surgeon and professor, was one of the experts behind that controversial, Kate Middleton-face study.) In their most famous paper—2008’s “Evolving Attractive Faces Using Morphing Technology and a Genetic Algorithm: A New Approach to Determining Ideal Facial Aesthetics”—the duo employed digital morphing software to “evolve” and “breed” more attractive faces over time based on data gathered from varied, human sources including Facebook surveys, plastic surgeons, student study participants, and professionals from an eyebrow cosmetics company favored by Kylie Jenner. In the most basic sense, their work tried to deploy predictive computing in a similar way to how scientists generate climate models… except they were hoping to see whether an average evolved over time into an ideal face.
Ultimately, Wong and Popenko determined that an “average” face didn’t make for a beautiful face. In fact, nasal width, eyebrow arch height, and lip fullness correlated significantly with the study’s scores of attractiveness. In other words, Jenner was onto something with her Kylie Lip Kit (designed to give you full, pouty lips) and heavily arched eyebrows (brought to you by Anastasia Brows). It turns out beauty, at least the kind that makes you want to shop at Sephora, isn’t determined by evolution—it’s determined by celebrity idols.
As this type of research has continued, businesses have sought to get in on the premise of technologically-defined beauty. The venture-backed Naked 3D Fitness Tracker is a $400 smart mirror (available for pre-order) that scans your body in 3D and uses a heat map to tell you where you’re growing muscle or gaining fat, and it claims results within 2.5 percent accuracy. It comes with a mirror that “looks” at you—a literal “Mirror, Mirror, On the Wall” situation—and encourages you to face the facts: Are you actually losing weight? This scale claims it won’t let you cheat.
Or, launched last April, an app called Map My Beauty claims to use facial zone recognition algorithms to objectively assess beauty. Users upload selfies, and the app spits out how and where to put on makeup. So far in its short existence, the app has proven particularly useful for advanced techniques like contouring, the old school method made viral by Instagram and the Kardashians. (Contouring requires a solid understanding of your own facial structure in order to manipulate appearance using light and shading.)
“Using this active appearance model and applying it to selfies we have never seen before, we can extract a handful of parameters which also—among others—describe implicitly facial attractiveness,” says Dr. Kristina Scherbaum, the computer scientist behind the app. What those parameters are, however, remains a secret. Map My Beauty has business aspirations beyond aiding at-home makeup artists. The team has previously worked with international beauty retailer Sephora, and now Map My Beauty has its own team of professional makeup artists. These professionals act as a focus group for labeling and categorizing the database, and Map My Beauty says its judgment criteria is proprietary.
This means an app may spit out the answers, but a team of humans is again behind the scenes making decisions (with varying degrees of subjectivity and objectivity). So from DaVinci to Wong and Popenko to this, that undeniable human element ultimately permeates results no matter how many layers of technology are added.
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