Fake Face Generator: Generating Fake Human Faces using GAN

Abstract

As machine learning is growing rapidly, creating art and images by machine is one of the most trending topics in current time. It has enormous applications in our current day to day life. Various researchers have researched this topic and they try to implement various ideas and most of them are based on CNN or other tools. The aim of our work is to generate comparatively better real-life fake human faces with low computational power and without any external image classifier, rather than removing all the noise and maximizing the stabilization which was the main challenge of the previous related works. For that, in this paper, we tried to implement our generative adversarial network with two fully connected sequential models, one as a generator and another as a discriminator. Our generator is trainable which gets random data and tries to create fake human faces. On the other hand, our discriminator gets data from the CelebA dataset and tries to detect that the images generated by the generator are fake or real, and gives feedback to the generator. Based on the feedback the generator improves its model and tries to generate more realistic images