Face Lit; AI generated faces
Imagine the number of pre digital photos that have been taken since the invention of the camera. With these there are both good and bad pictures with people not smiling, frowning or just looking ugly. No matter what made that picture special, that instance in time, these disappointments let the viewer down.
Thankfully Face Lit has the answer, you now can scan in your old photos and change them with natural moods and expressions being added without needing to know anything about altering photos or a professional. This novel application works through the use of artificial intelligence that creates AI generated faces from the original target image. Through simple presets you can get each person to look how they would have in the photo unlike what you have seen in the original.
Imagine all of those family pictures now fixed and frameable that were not originally possible, keeping precious moments alive. AI generated faces additionally offer you the opportunity to also fix media pictures, educational and historical pictures and also give you a way to use them in advertising.
So how does Face Lit work? The software takes an image and uses artificial intelligence that has been trained to recognize facial features. These features need to be also identified in context of the larger image such as the turn of the head, and relies on modelling of different layers of a face. These layers have parametric variables that are then modified through relative calculation and mapping from the original image.
Artificial intelligence utilizes essentially a weighted decision-making matrix that has node points at each layer and data is passed from one side of the neural net or decision-making matrix to the other, filtering through these decision layers as it goes. Each node that represents one of many options in each layer has a weighted value to help the software guess what it needs to make a decision on. A layer is essentially a data filter, for images it could be a method used for feature recognition, applying grayscale and contrast to assess a picture’s geometry through normal distribution of hue and relative pixel locations. AI generated faces and applications such as Face Lit normally use this type of technique and others in different layers to help understand what is in the picture automatically and where these features that have been identified are in terms of pixels.
The first step of the AI decision making process is to first take images and train it iteratively through passing images en masse through the neural network. Each time it correctly or incorrectly guesses each node the weighting factor is adjusted based on its previous experience. Eventually the weighting values of each node is within an acceptable tolerance and the learning stage is complete. For neural networks with multiple layers this is called deep learning however it is more representative of an iterative optimization process. This process is normally done during the development cycle of the software allowing users just to pass in their photos later and for it to only need to run once. The initial learning process normally requires a lot of processing power to optimize the weightings quickly, however once done these values are saved and the user can run Face Lit within a fraction of a second. This application is based on AI used for real-time video sources and therefore is highly performant for working quickly on lightweight low power devices.
The Future
Soon as Face Lit becomes much more widely used there will be no more need to hide hideous pictures that capture an instance in time. You can also use these images in advertising campaigns, education and media to help get your message across.
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