3D Gaze tracking by combining eye- and facial-gaze vectors

Kim, Byoung Cheul; Ko, Daejune; Jang, Unsoo; Han, Hyuksoo; Lee, Eui Chul

doi:10.1007/s11227-016-1817-5

Cited by 4 publications

(2 citation statements)

References 11 publications

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“…Ref. [13] proposed an eye-tracking system with the aim of estimating where a person is looking on a monitor through the use of the Kinect v2. This device is provided with an RGB camera, a depth camera, and a native function, called the high-definition face model, for detecting facial landmarks in the image plane.…”

Section: Model-based Techniquesmentioning

confidence: 99%

Eye-Tracking System with Low-End Hardware: Development and Evaluation

Iacobelli,

Ponzi,

Russo

et al. 2023

Information

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Eye-tracking systems have emerged as valuable tools in various research fields, including psychology, medicine, marketing, car safety, and advertising. However, the high costs of the necessary specialized hardware prevent the widespread adoption of these systems. Appearance-based gaze estimation techniques offer a cost-effective alternative that can rely solely on RGB cameras, albeit with reduced accuracy. Therefore, the aim of our work was to present a real-time eye-tracking system with low-end hardware that leverages appearance-based techniques while overcoming their drawbacks to make reliable gaze data accessible to more users. Our system employs fast and light machine learning algorithms from an external library called MediaPipe to identify 3D facial landmarks. Additionally, it uses a series of widely recognized computer vision techniques, like morphological transformations, to effectively track eye movements. The precision and accuracy of the developed system in recognizing saccades and fixations when the eye movements are mainly horizontal were tested through a quantitative comparison with the EyeLink 1000 Plus, a professional eye tracker. Based on the encouraging registered results, we think that it is possible to adopt the presented system as a tool to quickly retrieve reliable gaze information.

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Section: Model-based Techniquesmentioning

confidence: 99%

Eye-Tracking System with Low-End Hardware: Development and Evaluation

Iacobelli,

Ponzi,

Russo

et al. 2023

Information

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show abstract

“…Swirski 等 [5] 利用眼球几何结构特征和瞳孔运动模型, 从一组连续的眼睛图像中拟合眼球的立体运动状态, 进而估计出眼球的 3D 朝向. Kim 等 [6] 利用 Kinect v2 实现人脸模型估计, 获得人脸注视向量, 并估计眼球的 3D 旋转中心, 二者共同决定了 3D 眼睛注视向量. Lu 等 [7] 使用没有红外光照射的眼部图像和模型驱动的虹膜轮廓拟合, 对 3D 注视方向进行估计.…”

Section: 相关工作unclassified

Long Distance 3D Gaze Estimation Combined with Radar Target Detection

Zhang¹,

Duan²,

Zhu³

et al. 2020

J Comput-Aided Design Comput Graphics

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The 3D gaze estimation method based on 2D mapping and two lines of sight intersection has high accuracy in angle estimation. However, there are still large errors in the estimation of gaze distance. Thus, a 3D gaze estimation method based on an auxiliary millimeter wave radar target detection is proposed. First, it uses a movable calibration object to form a virtual calibration plane to calibrate the 2D planar fixation point.Next, the 2D fixation points of the right and left eyes on the calibration plane are calculated by the two-dimensional mapping function, and the 3D gaze direction is calculated combining the position of the eyes. Finally, the candidate point which has the smallest error from the gaze angle is selected from the target points detected by radar. A virtual plane is constructed at the distance of this point, by which the left and right lines of sight are intercept, the midpoint of the two intercept points is the estimation result of 3D fixation point. An experimental device is designed to fuse millimeter wave radar, camera and mobile calibration target. The 3D gaze point is tested in the range of 4.0~10.2 m. Experiments shows that with this method, the average angle error is 0.9°, the average Euclidean distance error is 130 mm. The result is better than binocular data geometric mapping method and depth camera aided estimation method.

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Monocular Gaze Estimation Network based on Mixed Attention

Zichen

Wei²,

et al. 2022

2022 3rd International Conference on Computer Vision, Image and Deep Learning &Amp; International Conference on Computer Engine

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3D Gaze tracking by combining eye- and facial-gaze vectors

Cited by 4 publications

References 11 publications

Eye-Tracking System with Low-End Hardware: Development and Evaluation

Eye-Tracking System with Low-End Hardware: Development and Evaluation

Long Distance 3D Gaze Estimation Combined with Radar Target Detection

Monocular Gaze Estimation Network based on Mixed Attention

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