Convolutional Neural Network-Based Methods for Eye Gaze Estimation: A Survey
Convolutional Neural Network-Based Methods for Eye Gaze Estimation: A Survey
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DOI：
10.1109/access.2020.3013540
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发表时间：
2020-01-01
期刊：
影响因子：
3.9
通讯作者：
Blignaut, Pieter
中科院分区：
文献类型：
--
作者：
Akinyelu, Andronicus A.;Blignaut, Pieter
眼动跟踪正在成为许多领域的一个非常重要的工具，包括人机交互，心理学，计算机视觉和医疗诊断。已经使用了不同的方法来解决眼动跟踪问题，然而，其中一些方法在现实条件下是不准确的，而另一些方法则需要明确的用户校准，这可能是繁重的。这些方法中的一些遭受差的图像质量和可变的光条件。最近深度学习的成功和流行极大地提高了眼动跟踪的性能。大规模数据集的可用性进一步提高了基于深度学习的方法的性能。本文介绍了基于深度学习的凝视估计技术的当前最新技术水平，重点是卷积神经网络（CNN）。本文还提供了对其他基于机器学习的凝视估计技术的调查。这项研究旨在为研究界提供有价值和有用的见解，这些见解可以加强改进和有效的基于深度学习的眼动跟踪模型的设计和开发。本研究还提供了有关各种预训练模型、网络架构和开源数据集的信息，这些信息对训练深度学习模型非常有用。
Eye tracking is becoming a very important tool across many domains, including human-computer-interaction, psychology, computer vision, and medical diagnosis. Different methods have been used to tackle eye tracking, however, some of them are inaccurate under real-world conditions, while some require explicit user calibration which can be burdensome. Some of these methods suffer from poor image quality and variable light conditions. The recent success and prevalence of deep learning have greatly improved the performance of eye-tracking. The availability of large-scale datasets has further improved the performance of deep learning-based methods. This article presents a survey of the current state-of-the-art on deep learning-based gaze estimation techniques, with a focus on Convolutional Neural Networks (CNN). This article also provides a survey on other machine learning-based gaze estimation techniques. This study aims to empower the research community with valuable and useful insights that can enhance the design and development of improved and efficient deep learning-based eye-tracking models. This study also provides information on various pre-trained models, network architectures, and open-source datasets that are useful for training deep learning models.