  Face inpainting, the technique of restoring missing or damaged regions in
facial images, is pivotal for applications like face recognition in occluded
scenarios and image analysis with poor-quality captures. This process not only
needs to produce realistic visuals but also preserve individual identity
characteristics. The aim of this paper is to inpaint a face given periocular
region (eyes-to-face) through a proposed new Generative Adversarial Network
(GAN)-based model called Eyes-to-Face Network (E2F-Net). The proposed approach
extracts identity and non-identity features from the periocular region using
two dedicated encoders have been used. The extracted features are then mapped
to the latent space of a pre-trained StyleGAN generator to benefit from its
state-of-the-art performance and its rich, diverse and expressive latent space
without any additional training. We further improve the StyleGAN output to find
the optimal code in the latent space using a new optimization for GAN inversion
technique. Our E2F-Net requires a minimum training process reducing the
computational complexity as a secondary benefit. Through extensive experiments,
we show that our method successfully reconstructs the whole face with high
quality, surpassing current techniques, despite significantly less training and
supervision efforts. We have generated seven eyes-to-face datasets based on
well-known public face datasets for training and verifying our proposed
methods. The code and datasets are publicly available.
