Have you been lately flooded with different posts on your social media profiles showing people with their current age on the left and their older self on the right? If yes and If you are wondering how people are doing it, then I may have an answer to your query here. There is a new app (only available on ios for now), FaceApp, which is behind this rising trend on social media.
My initial guess was about that it must be one of the new image filter rolled out by Snapchat or Instagram but was clueless about the craze behind this trend. Since it’s similar to a 10-year challenge, rolled out by Facebook, earlier this year, there was no need for everyone to go crazy about this app. There are filters as well available which can make you look older. So why go bonkers over this new app?
If you look closely, results by FaceApp are much more realistic as compared to its image filter based counterparts in the Snapchat and Instagram. So what has FaceApp done which Snapchat and Instagram could not? We will try to answer this question in this blog. Before we proceed on to answer this question, let us understand what FaceApp is!
What is FaceApp?
FaceApp is an IOS image processing app like many others on the app store which can process your images and bring changes to it. FaceApp was developed and released in 2016 by a small team of Russian developers. You can bring a selfie or use an ancient image from your phone and use a filter to use the app. The present free filters contain a smile that shows you old or young, sex bends, face hairs or the classic Walter White looks from the Breaking Bad TV series. The app gives its paying customers extra filters including maquilas, glasses and facial hair to their faces.
Changes include all the basic properties like contrast, brightness and sharpness to complex features like adding a smile, changing gender or showing an older/younger version of your self.
As previously said, all these features were available already on different apps as well but what makes the FaceApp really a winner is stark photo-realism. These images look more real and true rather than some blurry filter images. FaceApp with its surprisingly accurate image manipulation tricks has been able to gather a lot of attention because of which it has clocked over more than a million downloads in just 2 days. Rather than putting some creative or cartoonist filters over the images, FaceApp aims at manipulating the features present in the face in such a manner that it feels almost true to believe!
How is it different from Instagram filters?
So what is FaceApp doing differently? FaceApp rather than using image stacking or pixel manipulation uses Artificial Intelligence to bring the changes in the facial features. FaceApp makes use of the generative adversarial networks to create these images which are again too much realistic to be believed. Suppose you have a task of making a person smile in an image where he is not smiling at all. To make a person smile, extending the lips is not the only criteria. One needs to change all the facial features like eyes stretch, cheeks appearance in order to make the transformation realistic.
FaceApp has achieved this using GANs and this is exactly what is making this app stand out from the crowd. As far as we do not know of any other goods or studies of comparable performance in this field, FaceApp is far ahead in terms of the technology used. In the other section, let us dig deeper into the technique used by FaceApp
Underlying technology: GANs(Generative Adversarial Neural nets)
A Generative network is a type of AI machine learning (ML) method composed of two networks in a zero-sum game context that are in conflict with each other. GANs are usually unattended, teaching themselves how to imitate any specified information allocation.
There are two neural networks, the generator and the discriminator, that compose a GAN. The generator is a kind of neural system that creates fresh cases of an item, and the discriminator is a sort of neural network that determine its identity or whether it resides in a dataset.
During the teaching phase, both the models try to compete, their losses push each other towards improving behaviour and is called backpropagation. The generator’s objective is to generate reasonable performance while the discriminatory’s objective is to define the counterfeit. The generator generates high-quality results, and the discriminator is superior with the flagging components having a double feedback loop.
The first stage in the development of a GAN is for the required yield to be identified and the original learning data based on these parameters collect. This information are randomized and entered into the generator until the fundamental precision of the output is achieved.
Afterwards, the produced pictures and the real information points from the original concept are transmitted into the discriminator. The discriminator filters and gives the chance to reflect the validity of each image between 0 and 1 (1 corresponds to actual and 0 to falsify).
These values are then controlled manually and repeated until the required result is achieved. In a zero-sum game, both networks try to optimize a loss function.
Other use cases of GANs
- Generating realistic photographs
This use case revolves around generating new human faces which are too realistic that you won’t ever believe that there exists no person in the entire world with that face. Realism and use of state of the art artificial neural networks have made it possible to generate fake faces at ease. Realism in these images is the one key factor which has stirred the world and has made the use of GANs really attractive! You can view the example here which generated random faces using the generative adversarial networks
- Image-to-Image Translation
The image-to-image translation is the task of taking and transforming images from one domain to have the image style (or characteristics) from another.
- Text-to-Image Translation
In recent times, translation from text to the image has been an active field of research. A network’s capacity to understand the significance of a phrase and to create a precise picture depicting the phrase demonstrates a model’s capacity to more like people. Generative Adversarial Networks (GANs) are used to produce high-quality text input pictures through popular text to image translation techniques.
Proceed with caution
Experts have warned to allow them access to your personal details and identity by a FaceApp ancient age filter. FaceApp also involves in its terms and conditions, which you give the approval to access your picture gallery, the right of modifying, reproducing and publishing any image you process via its AI. This implies your face might finally be marketed.
By accepting the terms, we allow FaceApp to use, adapt, post, and distribute your user content in any media format on a perpetual, irrevocable, free of charge. This implies that you can use your actual name, your username or “any similarity is given” without notification, much less payment, in any format. You can keep it as long as you want and you can’t prevent it, even after you delete an app.
In this blog, we looked at the new emerging FaceApp which has gathered eyeballs all around. Furthermore, we also spent some time in understanding an overview of the technology which is behind this trending application. Although the faceApp has come with a breakthrough in the GANs technology and have set up high standards for the other apps, concerns around the data security and data-policy is still a concern with FaceApp.
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