@inproceedings{811470decc9c4faa835b79f33c304f7f,
title = "Gaze Estimation in the Dark with Generative Adversarial Networks",
abstract = "In this paper, we propose to utilize generative adversarial networks (GANs) to achieve successful gaze estimation in interactive multimedia environments with low light conditions such as a digital museum or exhibition hall. The proposed approach utilizes a GAN to enhance user images captured under low-light conditions, thereby recovering missing information for gaze estimation. The recovered images are fed into the CNN architecture to estimate the direction of user gaze. The preliminary experimental results on the modified MPIIGaze dataset demonstrated an average performance improvement of 6.6 under various low light conditions, which is a promising step for further research.",
keywords = "deep learning, GAN, Gaze estimation, low-light environment",
author = "Kim, \{Jung Hwa\} and Jeong, \{Jin Woo\}",
note = "Publisher Copyright: {\textcopyright} 2020 ACM.; 2020 ACM Symposium on Eye Tracking Research and Applications, ETRA 2020 ; Conference date: 02-06-2020 Through 05-06-2020",
year = "2020",
month = jun,
day = "2",
doi = "10.1145/3379157.3391654",
language = "English",
series = "Eye Tracking Research and Applications Symposium (ETRA)",
publisher = "Association for Computing Machinery",
editor = "Spencer, \{Stephen N.\}",
booktitle = "Proceedings ETRA 2020 Adjunct - ACM Symposium on Eye Tracking Research and Applications, ETRA 2020",
}