This file right here, is an AI model that can rate your face for its facial attractiveness.It
gave Angelina Jolie a 4.7/5 and chinese actress Dilraba Dilmurat a 4.8/5.
It gave these two unidentified faces scores in the low 2s.
It’s around 100mb and like most AI, and is stored in a vextension.
What you’re looking at is a near perfect snapshot of all of the neurons, biases and
weights in an AI algorithm’s brain being picture-frozen to share and reuse by different
researchers, but there’s one question most of you want answered…
why does it exist?
What benefit does this serve?
In 2020, a note was leaked from Tiktok to suppress content from those deemed to be too
ugly, poor or disabled for the platform.
They specified, abnormal body shape, chubby, obvious beer belly, obese or too thin (not
limited to dwarfism and acromegaly).
They added ugly facial looks, wrinkling, scarring, deformities among other things such as poor-looking
backgrounds or low production quality justifying it by saying that this is not the content
and brand image it wants to recommend to new users.If you scroll through your For-you-page
as a new account, you’ll notice that it’s the exact opposite of that, attractive men
and women in their hype house mansions and it’s certainly by autonomous design.
A.I. that rates people’s beauty would serve Tiktok’s purpose of pushing attractive faces
and content to the top of a social media app for greater engagement.
We as consumers are also to blame for Tiktok wanting to push more attractive people to
the top of the app.
If we didn’t engage with it as much, it wouldn’t be a priority, but we’re human
and biology dictates that from babies to grown adults, attractive faces do get more attention.
Tiktok could replace a team of human moderators with a highly precise algorithm that works
instantaneously upon video upload, embed the results from that into the video metadata
and then have a second algorithm push content based on the video’s data.
In doing so, the beautiful faces get a boost to rise to the top essentially recreating
the bias of pretty privilege we see in reality.
It’s not impossible for underfunded or less attractive creators to rise to the top, but
apps like Tiktok will see to it that you have to work twice as hard.
In A.I.
you hear a lot about reinforcing biases, and this is certainly the biggest challenge for
any beauty-related project.
Any good researcher will have a couple of safeguards in place to ensure the data going
in is as high quality and objective as can be.
Like all datascience models A.I.
needs to be trained on a database to make predictions, in this case SCUT FBP by chinese
researchers Xie and colleagues.
70 Volunteers rate each of the 5500 faces in the database and they have a 10% sampling
rate, meaning that 10% of the images are randomly selected to make sure there is consistency
in the ratings.
Unsurprisingly, there is consistency in the ratings because beauty is not in the eye of
the beholder.
The data shows a bell-curve distribution of ratings, as is common for most human traits
such as athleticism, fitness, intelligence, height, you name it.
The 70 raters produced a skewed distribution of attractiveness, implying that there are
more below average looking people in a random sample than there are above average.
Farhad Naini, in his book “Facial Aesthetics & Clinical Diagnosis” explains that beauty
is both subjective and objective.
It is objective when it comes to physical deformities, asymmetries, malformations or
facial flaws.
This is why we see more people in this section of the graph, all of which are characterised
by deformities, meaning there is high agreement between raters when they gave them their lower
ratings.
On this half however, beauty is subjective because they’re all attractive, free of
asymmetries or structural and cosmetic flaws, meaning they’re different variations of
similarly handsome faces, and so there is indeed more spread in the data.
Like it or not, we all subconsciously rate other human faces in an unspoken hierarchy.
You may not be actively aware of it, but it certainly exists, and for most, this manifests
itself as a phenomenon called the Halo effect.
In short, people assume positive traits from better looking individuals, implying that
we have rated their looks in some way or another and for enough people to think the same way
about a handsome face, implies consensus, just like when the volunteers were rating
for their database.
A.I. that can rate people’s looks are really just trying to mimic what we as humans already
do, because the best A.I. is actually just another human.
A.I. that is not scarily advanced would not have been able to beat Garry Kasparov in chess
or Lee Sedol in Go.
But most A.I.
researchers go about building these types of beauty A.I. in the wrong way, scarring
off the public and creating fear-mongering headlines.
In beauty A.I there are two types of architectures: Specific and Holistic models.
Specific models try to define what a beautiful face is by some kind of biological feature.
Psychology research shows that acne is a detriment to your physical attractiveness, so it’s
logical to design an A.I. that can detect acne and rank it lower.
Some try to use facial geometry using facial landmarks.
Given 70 facial landmarks and a set of ratios that attractive faces have, how close are
your values to the attractive face?
This is a flawed method because it forces beauty into a narrow band even if you use
many different racial groups, they all somewhat converge to the same, uncanny face.
Other’s use Gabor functions, invented by this gentleman to measure skin roughness and
blemishes, but again, this is a one dimensional view of beauty.
There are plenty of fashion models that would rate higher than the average person even if
they had blemished skin.
Holistic A.I.
consider the face as a whole.
A rater can say that this face is a 5/5 and it is upto the A.I.
network to figure out what features about this arrangement of pixels makes it so attractive
to humans.
Do this enough times and the A.I.
can pickup on strange markers of beauty we cannot even think to define.
If there are many different looking but still beautiful faces in the dataset, like there
are in real-life, then the A.I.
can begin to learn what is similar or dissimilar between attractive faces.
In the case of Angelina and Dilraba, both have clear skin, low asymmetry and healthy
structural development, two very different aesthetics, but almost equally as attractive
according to the A.I.
As a whole, beauty scoring to me is an ineffective use of A.I.
If you want to truly find the most attractive faces to promote on Tiktok, just ask your
audience, they’ll engage more with the pretty faces anyway, there is no need for an A.I.
to moderate on top of that.
A better use of A.I. is in diagnostics.
AI that can help people diagnose and improve their looks is far more useful than turning
this data into an arbitrary score.
At QOVES we’ve built one such tool that uses a holistic model of many different racial
groups, ages and genders to identify skin characteristics and formulate highly specific
skincare to match, if you want to play around with that, visit the QOVES website.