This article presents an evolutionary approach for synthetic human portraits generation based on the latent space exploration of a generative adversarial network. The idea is to produce different human face images very similar to a given target portrait. The approach applies StyleGAN2 for portrait generation and FaceNet for face similarity evaluation. The evolutionary search is based on exploring the real-coded latent space of StyleGAN2. The main results over both synthetic and real images indicate that the proposed approach generates accurate and diverse solutions, which represent realistic human portraits. The proposed research can contribute to improving the security of face recognition systems.
@article{arxiv.2204.11887,
title = {Evolutionary latent space search for driving human portrait generation},
author = {Benjamín Machín and Sergio Nesmachnow and Jamal Toutouh},
journal= {arXiv preprint arXiv:2204.11887},
year = {2022}
}
Comments
This paper was accepted and presented during the 2021 IEEE Latin American Conference on Computational Intelligence (LA-CCI)