It is said that beauty is in the eye of the beholder, but in reality, it goes much deeper than that.
The concept of physical beauty resides in the mind, defined by any features that we find attractive on the faces of others. These subtle preferences are some of our most private inner thoughts – but that doesn’t mean they can’t be monitored and maybe even predicted.
In a new study, researchers used electroencephalography (EEG) measurements to identify what kind of facial features turned out to be attractive and then fed the results of an artificial intelligence (AI) program.
The machine learning system – called a contradictory generative neural network (GAN) – was first able to familiarize itself with what kind of individual faces they found desirable and then make others completely designed specifically to satisfy you: visions adapted by synthesized beauty, as unattainable as they were perfect.
The experiment, led by a team of psychologists and computer scientists from the University of Helsinki in Finland, was about a massive Tinder session for the 30 volunteers who participated.
Except for a few big differences.
While the participants were sitting in front of a computer screen showing them a series of faces, none of the faces displayed were real people, but were realistic-looking artificial portraits generated from a data set of about 200,000 images of the stars.
Unlike regular Tinder use, participants also wore elastic caps with electrodes designed to measure their brain activity while looking at their faces. They also didn’t have to slide right when they saw someone they liked the look of – everything was taken care of.
“All they had to do was look at the pictures,” explains cognitive neurologist Michiel Spapé. “I measured their brain’s immediate response to images.”
These individual measurements of neural activity were then evaluated by GAN, which was able to interpret the brain’s responses in terms of how attractive each artificial face was considered by the viewer.
Using this data, GAN was then able to generate new faces informed by people’s EEG attraction identifiers.
In a second experiment, these newly invented faces were then displayed back to the volunteers, who rated them in terms of attractiveness, along with other images of the randomly generated faces.
Finally, the results validated the researchers’ test, with participants evaluating the images adapted to be attractive in about 80% of cases, while the other faces were selected only 20% of the time.
Although this is just a small study, it is just another example of how refined AI systems become in the understanding of what makes us tick – even in intimate and often unspoken notions, such as the realm of personal attraction.
“Success in assessing attractiveness is particularly significant because it is such a turbulent psychological property of stimuli,” says Spapé.
“If this is possible in something that is as personal and subjective as attractiveness, we may be able to analyze other cognitive functions, such as perception and decision making. understand individual differences. “
The findings are reported in IEEE transactions on emotional calculation.