How does the human brain decide someone is beautiful? It is something universal or subjective? This AI attempts to help give us some answers to these questions.
This AI reads your brain to generate personally attractive faces. It generates images containing optimal values for personal attractive features on a human face!
Watch the video
References
DeepFakes in 5 minutes: https://youtu.be/Upv0kMLx7xI
GANs explained: https://youtu.be/ZnpZsiy_p2M
An amazing application of GANs: https://youtu.be/zB_jQ8SUjKE
Read the article: https://whats-ai.medium.com/
M. Spape, K. Davis, L. Kangassalo, N. Ravaja, Z. Sovijarvi-Spape and T.
Ruotsalo, "Brain-computer interface for generating personally attractive
images," in IEEE Transactions on Affective Computing, doi:
10.1109/TAFFC.2021.3059043.
Abstract—
GANs explained: https://youtu.be/ZnpZsiy_p2M
An amazing application of GANs: https://youtu.be/zB_jQ8SUjKE
Read the article: https://whats-ai.medium.com/
M. Spape, K. Davis, L. Kangassalo, N. Ravaja, Z. Sovijarvi-Spape and T.
Ruotsalo, "Brain-computer interface for generating personally attractive
images," in IEEE Transactions on Affective Computing, doi:
10.1109/TAFFC.2021.3059043.
Abstract—
While we instantaneously recognize a face as attractive, it is
much harder to explain what exactly defines personal attraction. This
suggests that attraction depends on implicit processing of complex,
culturally and individually defined features.
much harder to explain what exactly defines personal attraction. This
suggests that attraction depends on implicit processing of complex,
culturally and individually defined features.
Generative adversarial neural networks (GANs), which learn to mimic complex data distributions, can potentially model subjective preferences unconstrained by pre-defined model parameterization. Here, we present generative brain-computer interfaces (GBCI), coupling GANs with brain-computer interfaces.
GBCI first presents a selection of images and captures personalized attractiveness reactions toward the images via electroencephalography. These reactions are then used to control a GAN model, finding a representation that matches the features constituting an attractive image for an individual.
We conducted an experiment (N=30) to validate GBCI using a face-generating GAN and producing images that are hypothesized to be individually attractive. In double-blind evaluation of the GBCI-produced images against matched controls, we found GBCI yielded highly accurate results.
Thus, the use of EEG responses to control a GAN presents a valid tool for interactive information-generation. Furthermore, the GBCI-derived images visually replicated known effects from social neuroscience, suggesting that the individually responsive, generative nature of GBCI provides a powerful, new tool in mapping individual differences and visualizing cognitive-affective processing.
Video Transcript:
00:00
i think you will all agree that when you
00:02
look at someone
00:03
and find the person attractive you
00:05
cannot explain why
00:07
there are many reasons involved in this
00:09
decision
00:10
plus some of these reasons may not even
00:12
be directly related to the physique of
00:14
the person
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still when you look at a picture of
00:17
someone you instantly know if the person
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is attractive or not by your standards
00:21
and you know this without any doubts but
00:24
how does that happen
00:26
can we explain or understand what beauty
00:28
is to us and worse can someone else
00:31
understand what we find beautiful is
00:34
beauty even something universal
00:36
or subjective to me it's definitely
00:39
something subjective
00:40
just like art as we say beauty is in the
00:43
eye of the beholder
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isn't it but then why specific people
00:47
become
00:48
actors or models and not others is there
00:51
a universal beauty that we could explain
00:53
is beauty something cultural biological
00:56
or a blend of both
00:58
i think these are mostly philosophical
01:00
debates that we can discuss in the
01:01
comments
01:02
for those of you that will like to i
01:04
would love to chat and even debate about
01:06
this with you
01:07
anyways back to what we know about
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beauty as we know
01:10
humans typically respond emotionally to
01:13
attractive images
01:14
rather than purely based on rational
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visually salient reasons
01:18
which is what makes beauty so subjective
01:20
but is there a way to understand what we
01:22
think
01:23
of beauty and use this information to
01:25
create attractive pictures
01:27
can we understand our brain signals to
01:29
achieve that
01:30
and is it even readable to these signals
01:33
well
01:34
this is what michel spape and his team
01:36
from the university of helsinki
01:38
attempted to do what you see here is
01:41
called electroencephalography
01:43
or eeg it's a monitoring method used to
01:46
record electrical brain activity
01:48
using electrodes placed along the scalp
01:51
specifically they tracked
01:52
two different channels with these
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electrodes which are known to be
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responsive to stimuli that are either
01:58
novel or effectively evocative or
02:01
mentally demanding and related to
02:03
working memory updating
02:05
meaning that the stimuli are improbable
02:07
and require a mental or physical
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response
02:10
but they employed two strategies in
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their experiments
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first they made the supposed attractive
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images relatively improbable to appear
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they also asked the participants to
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focus on attractive faces
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by mentally counting their occurrence to
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me these strategies seem like a
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considerable bias to detecting
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actual attractiveness where the signal
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responses mainly tell us two things
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one that the person is lightly surprised
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because the attractive pictures are rare
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and two that the person is mentally
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active trying to remember the count and
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add to it
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i personally see it as an overkill way
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of asking whether the person finds the
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face attractive or not
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since it does not really measure the
02:50
beauty of the image scene
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and here i am not entering about the
02:54
details of the data set they used to
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train their model
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which is only made of celebrities
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nonetheless
03:00
this was undoubtedly a great way to find
03:03
out
03:03
which faces were attractive or not to
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train their model to focus on beautiful
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faces
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which is a very cool application of guns
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as you can see it clearly worked out
03:13
there's a significant difference between
03:15
responses after a slight delay of time
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starting approximately a fourth of a
03:19
second after showing the image
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where the pink lines are the two channel
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responses i just mentioned
03:25
are shown here the top shows the frontal
03:28
response
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responsible for the novel and
03:31
effectively evocative stimuli while the
03:33
bottom is the parietal response
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responsible for mentally demanding
03:37
stimuli here
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the faces deemed attractive are green
03:40
unattractive in red
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and inconsistent responses in gray these
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inconsistent responses signified that
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the data
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was not used due to its low level of
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confidence
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attractiveness was confident when both
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electrodes evoked more positivity for
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the attractive faces
03:57
now let's see how they created such a
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model to use their brain activity to
04:01
generate attractive faces
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at first they needed to generate random
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images
04:06
to show the test subjects to do that
04:08
they used a gun model trained on 200 000
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images of celebrity faces already
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introducing a bias
04:15
but still let's keep going they then had
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a latent space
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from which they could use to generate
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new artificial images
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this is just like any other gun model
04:25
like deep fakes
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it is very similar to what i already
04:28
explained in previous videos
04:29
and you are most certainly aware of how
04:31
it works so i won't cover it again here
04:34
but if you are not
04:35
you are free to click on the top right
04:37
pop-up and watch my explanations of
04:39
gan architectures and i'll talk a little
04:41
bit more about this latent space
04:43
later on then each participant was asked
04:47
to go through
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randomly generated images remember here
04:51
they had to count when they see an
04:52
attractive face mentally
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once the results are all compiled they
04:56
train a classifier on these brain output
04:59
signals
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containing the image's subject
05:01
assessments
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basically ideally containing whether the
05:05
face was attractive or not
05:07
once the classifier is trained it's used
05:09
under brain electrical responses
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on randomly generated images note that
05:14
all these randomly generated images come
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from a latent space originally
05:19
created by the encoder of the gun
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trained on the first step
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as you can see here the latent space is
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a space where one point represents
05:27
an input the generator can use to create
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a unique image
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this space contains all the faces that
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this generator can make
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thus when an image is generated using a
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point in the space
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or feature vector it can be considered
05:41
attractive or not
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by the classifier if that's the case it
05:44
will encourage the network to create
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images by selecting the generator's
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input close to this point in the space
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focusing the generator's attention on a
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subspace
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where the features of the generated
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faces are pleasing to the participant
05:58
finally this is how new images
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containing optimal values for personal
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attractive features on a human face
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are generated i hope you enjoyed this
06:07
video
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and i definitely invite you to read
06:09
their paper it's very interesting and
06:12
well written it's the first link in the
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description don't forget to like and
06:15
subscribe if you enjoyed the video
06:17
and share your thoughts in the comments
06:19
where i will answer you in the following
06:21
7 minutes
06:22
i promise thank you for watching