Purpose

This paper primarily aims to focus on a review of convolutional neural network (CNN)-based eye control systems. The performance of CNNs in big data has led to the development of eye control systems. Therefore, a review of eye control systems based on CNNs is helpful for future research.

Design/methodology/approach

In this paper, first, it covers the fundamentals of the eye control system as well as the fundamentals of CNNs. Second, the standard CNN model and the target detection model are summarized. The eye control system’s CNN gaze estimation approach and model are next described and summarized. Finally, the progress of the gaze estimation of the eye control system is discussed and anticipated.

Findings

The eye control system accomplishes the control effect using gaze estimation technology, which focuses on the features and information of the eyeball, eye movement and gaze, among other things. The traditional eye control system adopts pupil monitoring, pupil positioning, Hough algorithm and other methods. This study will focus on a CNN-based eye control system. First of all, the authors present the CNN model, which is effective in image identification, target detection and tracking. Furthermore, the CNN-based eye control system is separated into three categories: semantic information, monocular/binocular and full-face. Finally, three challenges linked to the development of an eye control system based on a CNN are discussed, along with possible solutions.

Originality/value

This research can provide theoretical and engineering basis for the eye control system platform. In addition, it also summarizes the ideas of predecessors to support the development of future research.

中文翻译：

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基于卷积神经网络的眼控系统：综述

目的

本文主要旨在回顾基于卷积神经网络 (CNN) 的眼控系统。CNN 在大数据中的表现导致了眼控系统的发展。因此，回顾基于 CNN 的眼控系统有助于未来的研究。

设计/方法/方法

在本文中，首先，它涵盖了眼控系统的基础知识以及 CNN 的基础知识。其次，总结了标准的CNN模型和目标检测模型。接下来描述和总结眼控系统的 CNN 凝视估计方法和模型。最后，对眼控系统注视估计的进展进行了讨论和展望。

发现

眼控系统通过注视估计技术来完成控制效果，该技术关注眼球的特征和信息、眼球运动和注视等。传统眼控系统采用瞳孔监测、瞳孔定位、霍夫算法等方法。本研究将重点关注基于 CNN 的眼控系统。首先，作者介绍了CNN模型，该模型在图像识别、目标检测和跟踪方面是有效的。此外，基于 CNN 的眼控系统分为三类：语义信息、单目/双目和全脸。最后，讨论了与基于 CNN 的眼控系统开发相关的三个挑战以及可能的解决方案。

原创性/价值

本研究可为眼控系统平台提供理论和工程依据。此外，还总结了前人的思想，以支持未来研究的发展。