Face-Morphing using Generative Adversarial Network(GAN)

GAN has a very simple task to do, that is, to generate data from the scratch, data of a quality that can fool even humans. Invented by Ian Goodfellow and colleagues in 2014, this model consists of two Neural -- Networks(Generator and Discriminator) competing with one another resulting in the generation of some authentic content. The purpose of two Networks may be summarised as to learn the underlying structure of the input database as much as possible and using that knowledge to create similar content which fits all the parameters to fit in the same category. As shown above, the input was that of human faces, where it learned exactly what it is that makes a human face, well, human. Using that understanding it generated random human faces which otherwise might have been real as well.