@inproceedings{c2f8dcda32b24321afee369249dac710,
title = "FAU-Gaze: Fast and Accurate User-specific Gaze Estimation Framework",
abstract = "Gaze estimation has a wide range of applications such as neuroscience and clinical research. In this paper, we propose and implement a fast and accurate user-specific gaze estimation system, called FAU-Gaze. FAU-Gaze supports online real-time training with an inference speed of up to 7-11.5 ms in 100 FPS. Compared with existing models, the kernel model FPGC (Feature-based Personalized Gaze Calibrator) of FAU-Gaze increases the accuracy by 36.4\% and 33.7\% on MPIIFaceGaze and TabletGaze respectively. By mining each user's potential characteristics, FAU-Gaze can more accurately locate each user's real gaze position. In order to test FAU-Gaze, we also introduce a low-resolution and low-definition laptop gaze estimation dataset TobiiGaze containing 41,000 images. Through our experiments on both TobiiGaze, MPIIFaceGaze, and TabletGaze, the prediction error of FAU-Gaze is reduced to 1.61 cm and the robustness outperforms the state-of-the-art.",
keywords = "deep learning, eye appearance, gaze estimation",
author = "Ye Ding and Li Lu and Ziyuan Liu and Songjie Wu and Qing Liao",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 International Joint Conference on Neural Networks, IJCNN 2023 ; Conference date: 18-06-2023 Through 23-06-2023",
year = "2023",
doi = "10.1109/IJCNN54540.2023.10191966",
language = "英语",
series = "Proceedings of the International Joint Conference on Neural Networks",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "IJCNN 2023 - International Joint Conference on Neural Networks, Proceedings",
address = "美国",
}