This code is licensed under CC BY-NC-SA 4.0. Commercial usage is not permitted. If you use this dataset or the code in a scientific publication, please cite the following paper:
@inproceedings{FischerECCV2018,
author = {Tobias Fischer and Hyung Jin Chang and Yiannis Demiris},
title = {{RT-GENE: Real-Time Eye Gaze Estimation in Natural Environments}},
booktitle = {European Conference on Computer Vision},
year = {2018},
month = {September},
pages = {339--357}
}
This work was supported in part by the Samsung Global Research Outreach program, and in part by the EU Horizon 2020 Project PAL (643783-RIA).
The code is split into three parts, each having its own README contained. There is also an accompanying dataset (alternative link) to the code. For more information, other datasets and more open-source software please visit the Personal Robotic Lab's website: https://www.imperial.ac.uk/personal-robotics/software/.
The rt_gene directory contains a ROS package for real-time eye gaze estimation. This contains all the code required at inference time.
The rt_gene_inpainting directory contains code to inpaint the region covered by the eyetracking glasses.
The rt_gene_model_training directory allows using the inpainted images to train a deep neural network for eye gaze estimation.