GANs and artificial facial expressions in synthetic portraits

Abstract

Generative Adversarial Networks (GANs) provide powerful architectures for deep generative learning. GANs have enabled us to achieve an unprecedented degree of realism in the creation of synthetic images of human faces, landscapes, buildings, among others. Not only image generation, but also image manipulation is possible with GANs. Generative deep learning models are inherently limited in their creative abilities because of a focus on learning for perfection. We investigated the potential of GAN's latent spaces to encode human expressions, highlighting creative interest for suboptimal solutions rather than perfect reproductions, in pursuit of the artistic concept. We have trained Deep Convolutional GAN (DCGAN) and StyleGAN using a collection of portraits of detained persons, portraits of dead people who died of violent causes and people whose portraits were taken during an orgasm. We present results which diverge from standard usage of GANs with the specific intention of producing portraits that may assist us in the representation and recognition of otherness in contemporary identity construction.