How Do GANS Create Faces?

Artificial intelligence (AI) art is currently dominating news channels and the internet as more artists embrace it for its incredible abilities to generate photo-realistic images that are hard to differentiate from original ones. AI artwork is created by using programs commonly referred to as AI art generators.

Initially, companies that developed AI art generators like NightCafe Creator didn’t allow users to generate human faces because they were concerned about the possibility of misuse by unscrupulous users. As a result, editing AI-generated faces using these programs wasn’t possible. This changed when many software engineering companies improved the security of their systems by including algorithms that recognize and block attempts to generate images that go against their terms of use. 

Now, you can find AI art generators that create photo-realistic images with human faces. With an AI human face generator like NightCafe Creator, you can even generate versions of your own face. Many people, even those who aren’t professional artists, are using this technology to generate images of themselves for their social media profiles and other related applications.

AI art generators use generative adversarial networks (GANs) to learn and produce realistic images. It’s important to understand how this technology creates images, including human faces, to be able to use it most effectively.

How GANs Generate Human Faces

A GAN is a branch of machine learning technology that’s based on neural networks. Previous AI image generators were based on convolutional neural networks and recurrent neural networks, which use a single neural network. The introduction of GANs revolutionised the concept of machine learning because they bring together two competing neural networks (algorithms) called the generator and the discriminator.

The two neural networks are designed to perform opposing roles. The generator is responsible for creating Deepfakes, while the discriminator detects the Deepfakes. As the two compete against each other, they improve how the other functions. 

This means that the generator works very hard to trick the discriminator into accepting the Deepfakes as real. In doing so, the generator helps the discriminator become more proficient in detecting fake images. As the discriminator continues to improve its accuracy, it helps the generator produce even more realistic images. So, although the two algorithms are considered adversaries, they actually complement and help each other get better at what they do. These neural networks continue to learn from every input, making every output better than the previous one. 

Here are the steps taken by GANs to generate photo-realistic human faces:

  • The generator takes several random numbers with the same size as the seed number and creates an image.
  • The image, which has an equal number of real face images inputted during deep training, is taken through the discriminator for recognition and classification.
  • The discriminator then passes the results as a loss back to the generator for more accuracy. The generator must then make its fake images more accurate. The generator will finetune the images to try and trick the discriminator into believing that the images are real. This helps the AI face generator improve the quality of its images.
  • The process will repeat as many times as it takes until the discriminator accepts the images as real.

The two neural networks do not run simultaneously. The generator starts the process by generating the image and then sends the image to the discriminator for classification. After analysing the image, the discriminator sends it back to the generator for improvements.

GANs are known for being better at generating photo-realistic faces and other images because they’re not easily fooled into misclassifying images by making a few adjustments to the images; previous neural networks were easy to trick. With a GAN, the back and forth between the discriminator and generator continues until the final image is perfect.