Trends

AI Can Now Predict The Faces You Will Be Attracted To

The data was entered into an AI that generated new images i.e. hundred of artificial portraits using a generative adversarial neural network (GAN).

Ever wondered about whether you could understand how your mind feels attracted towards certain faces, and if you have control over it and understand the mechanism? Well, a new study makes this abstract idea a little closer to reality.

In a new study, researchers hailing from the University of Helsinki and the University of Copenhagen have made an AI that can analyze your brainwaves and understand all the kinds of faces you find attractive.

In February, IEEE Transactions in Affective Computing journal studied 30 volunteers through electroencephalography (EEG) and monitored their brainwaves to record the electrical activity of the brain. The volunteers were displayed images stitched from 200,000 images of real celebrities and recorded their brainwaves responses. The mechanism functioned like the famous dating app Tinder: the participants ‘swiped right’ when they wanted to reciprocate to an attractive face.

“Here, however, they did not have to do anything but look at the images. We measured their immediate brain response to the images.” stated Senior researcher and Docent Michiel Spapé from the Department of Psychology and Logopedics at the University of Helsinki.

However, in this experiment, the volunteers did not actually swipe right or left but the researchers predicted that on the basis of EEG readings that gave an insight into the faces they found attractive. Further, this data was entered into an AI that generated new images i.e. hundred of artificial portraits using a generative adversarial neural network (GAN). Thus for each volunteer, the researchers presented new portraits claiming that they would find these faces attractive. It was a positive result that the new images clearly matched with the personal choices of the people with an accuracy rate of 80 percent.

Spapé explained, “The study demonstrates that we are capable of generating images that match personal preference by connecting an artificial neural network to brain responses. Succeeding in assessing attractiveness is especially significant, as this is such a poignant, psychological property of the stimuli.”

'Computer vision has thus far been very successful at categorizing images based on objective patterns,' he added.

Science Alert reported, “The machine learning system – termed a generative adversarial neural network (GAN) – was first able to familiarise itself with what sorts of faces individual people found desirable, and then fabricate entirely new ones specifically designed to please: tailored visions of synthesized beauty, as unattainable as they were perfect.”

Academy research fellow and associate professor Tuukka Ruotsalo, who headed this project, stated, “A brain-computer interface such as this is able to interpret users’ opinions on the attractiveness of a range of images. By interpreting their views, the AI model interpreting brain responses and the generative neural network modeling the face images can together produce an entirely new face image by combining what a particular person finds attractive.”

Researchers believe this technique can be employed to study the unconscious attitudes and preferences of human beings. Spapé in a statement remarked, “If this is possible in something that is as personal and subjective as attractiveness, we may also be able to look into other cognitive functions such as perception and decision-making. Potentially, we might gear the device towards identifying stereotypes or implicit bias and better understand individual differences.”

Spapé further explained the observed pattern in previous models, that most in the experiment had a preference towards smiling blonde models.

“Attractiveness is a more challenging subject of study, as it is associated with cultural and psychological factors that likely play unconscious roles in our individual preferences,” Spapé said at the end of the statement. “Indeed, we often find it very hard to explain what it is exactly that makes something, or someone, beautiful: Beauty is in the eye of the beholder.”

Trends

AI Can Now Predict The Faces You Will Be Attracted To

The data was entered into an AI that generated new images i.e. hundred of artificial portraits using a generative adversarial neural network (GAN).

Ever wondered about whether you could understand how your mind feels attracted towards certain faces, and if you have control over it and understand the mechanism? Well, a new study makes this abstract idea a little closer to reality.

In a new study, researchers hailing from the University of Helsinki and the University of Copenhagen have made an AI that can analyze your brainwaves and understand all the kinds of faces you find attractive.

In February, IEEE Transactions in Affective Computing journal studied 30 volunteers through electroencephalography (EEG) and monitored their brainwaves to record the electrical activity of the brain. The volunteers were displayed images stitched from 200,000 images of real celebrities and recorded their brainwaves responses. The mechanism functioned like the famous dating app Tinder: the participants ‘swiped right’ when they wanted to reciprocate to an attractive face.

“Here, however, they did not have to do anything but look at the images. We measured their immediate brain response to the images.” stated Senior researcher and Docent Michiel Spapé from the Department of Psychology and Logopedics at the University of Helsinki.

However, in this experiment, the volunteers did not actually swipe right or left but the researchers predicted that on the basis of EEG readings that gave an insight into the faces they found attractive. Further, this data was entered into an AI that generated new images i.e. hundred of artificial portraits using a generative adversarial neural network (GAN). Thus for each volunteer, the researchers presented new portraits claiming that they would find these faces attractive. It was a positive result that the new images clearly matched with the personal choices of the people with an accuracy rate of 80 percent.

Spapé explained, “The study demonstrates that we are capable of generating images that match personal preference by connecting an artificial neural network to brain responses. Succeeding in assessing attractiveness is especially significant, as this is such a poignant, psychological property of the stimuli.”

'Computer vision has thus far been very successful at categorizing images based on objective patterns,' he added.

Science Alert reported, “The machine learning system – termed a generative adversarial neural network (GAN) – was first able to familiarise itself with what sorts of faces individual people found desirable, and then fabricate entirely new ones specifically designed to please: tailored visions of synthesized beauty, as unattainable as they were perfect.”

Academy research fellow and associate professor Tuukka Ruotsalo, who headed this project, stated, “A brain-computer interface such as this is able to interpret users’ opinions on the attractiveness of a range of images. By interpreting their views, the AI model interpreting brain responses and the generative neural network modeling the face images can together produce an entirely new face image by combining what a particular person finds attractive.”

Researchers believe this technique can be employed to study the unconscious attitudes and preferences of human beings. Spapé in a statement remarked, “If this is possible in something that is as personal and subjective as attractiveness, we may also be able to look into other cognitive functions such as perception and decision-making. Potentially, we might gear the device towards identifying stereotypes or implicit bias and better understand individual differences.”

Spapé further explained the observed pattern in previous models, that most in the experiment had a preference towards smiling blonde models.

“Attractiveness is a more challenging subject of study, as it is associated with cultural and psychological factors that likely play unconscious roles in our individual preferences,” Spapé said at the end of the statement. “Indeed, we often find it very hard to explain what it is exactly that makes something, or someone, beautiful: Beauty is in the eye of the beholder.”

Trends

AI Can Now Predict The Faces You Will Be Attracted To

The data was entered into an AI that generated new images i.e. hundred of artificial portraits using a generative adversarial neural network (GAN).

Ever wondered about whether you could understand how your mind feels attracted towards certain faces, and if you have control over it and understand the mechanism? Well, a new study makes this abstract idea a little closer to reality.

In a new study, researchers hailing from the University of Helsinki and the University of Copenhagen have made an AI that can analyze your brainwaves and understand all the kinds of faces you find attractive.

In February, IEEE Transactions in Affective Computing journal studied 30 volunteers through electroencephalography (EEG) and monitored their brainwaves to record the electrical activity of the brain. The volunteers were displayed images stitched from 200,000 images of real celebrities and recorded their brainwaves responses. The mechanism functioned like the famous dating app Tinder: the participants ‘swiped right’ when they wanted to reciprocate to an attractive face.

“Here, however, they did not have to do anything but look at the images. We measured their immediate brain response to the images.” stated Senior researcher and Docent Michiel Spapé from the Department of Psychology and Logopedics at the University of Helsinki.

However, in this experiment, the volunteers did not actually swipe right or left but the researchers predicted that on the basis of EEG readings that gave an insight into the faces they found attractive. Further, this data was entered into an AI that generated new images i.e. hundred of artificial portraits using a generative adversarial neural network (GAN). Thus for each volunteer, the researchers presented new portraits claiming that they would find these faces attractive. It was a positive result that the new images clearly matched with the personal choices of the people with an accuracy rate of 80 percent.

Spapé explained, “The study demonstrates that we are capable of generating images that match personal preference by connecting an artificial neural network to brain responses. Succeeding in assessing attractiveness is especially significant, as this is such a poignant, psychological property of the stimuli.”

'Computer vision has thus far been very successful at categorizing images based on objective patterns,' he added.

Science Alert reported, “The machine learning system – termed a generative adversarial neural network (GAN) – was first able to familiarise itself with what sorts of faces individual people found desirable, and then fabricate entirely new ones specifically designed to please: tailored visions of synthesized beauty, as unattainable as they were perfect.”

Academy research fellow and associate professor Tuukka Ruotsalo, who headed this project, stated, “A brain-computer interface such as this is able to interpret users’ opinions on the attractiveness of a range of images. By interpreting their views, the AI model interpreting brain responses and the generative neural network modeling the face images can together produce an entirely new face image by combining what a particular person finds attractive.”

Researchers believe this technique can be employed to study the unconscious attitudes and preferences of human beings. Spapé in a statement remarked, “If this is possible in something that is as personal and subjective as attractiveness, we may also be able to look into other cognitive functions such as perception and decision-making. Potentially, we might gear the device towards identifying stereotypes or implicit bias and better understand individual differences.”

Spapé further explained the observed pattern in previous models, that most in the experiment had a preference towards smiling blonde models.

“Attractiveness is a more challenging subject of study, as it is associated with cultural and psychological factors that likely play unconscious roles in our individual preferences,” Spapé said at the end of the statement. “Indeed, we often find it very hard to explain what it is exactly that makes something, or someone, beautiful: Beauty is in the eye of the beholder.”

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