Vision-Based Gaze Estimation: A Review

Wang, Xinming; Zhang, Jianhua; Zhang, Hanlin; Zhao, Shuwen; Liu, Honghai

doi:10.1109/tcds.2021.3066465

Cited by 14 publications

(2 citation statements)

References 119 publications

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“…Most existing popular gaze datasets are RGB images with high resolution. However, image blur is a factor that leads to inaccuracies in the analysis of human social interactions involving gaze [ 45 ]. Therefore, it is particularly necessary to explore gaze estimation on low-resolution images.…”

Section: Datasetmentioning

confidence: 99%

Model-Based 3D Gaze Estimation Using a TOF Camera

Shen,

Li,

Guo

et al. 2024

Sensors

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Among the numerous gaze-estimation methods currently available, appearance-based methods predominantly use RGB images as input and employ convolutional neural networks (CNNs) to detect facial images to regressively obtain gaze angles or gaze points. Model-based methods require high-resolution images to obtain a clear eyeball geometric model. These methods face significant challenges in outdoor environments and practical application scenarios. This paper proposes a model-based gaze-estimation algorithm using a low-resolution 3D TOF camera. This study uses infrared images instead of RGB images as input to overcome the impact of varying illumination intensity in the environment on gaze estimation. We utilized a trained YOLOv8 neural network model to detect eye landmarks in captured facial images. Combined with the depth map from a time-of-flight (TOF) camera, we calculated the 3D coordinates of the canthus points of a single eye of the subject. Based on this, we fitted a 3D geometric model of the eyeball to determine the subject’s gaze angle. Experimental validation showed that our method achieved a root mean square error of 6.03° and 4.83° in the horizontal and vertical directions, respectively, for the detection of the subject’s gaze angle. We also tested the proposed method in a real car driving environment, achieving stable driver gaze detection at various locations inside the car, such as the dashboard, driver mirror, and the in-vehicle screen.

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Section: Datasetmentioning

confidence: 99%

Model-Based 3D Gaze Estimation Using a TOF Camera

Shen,

Li,

Guo

et al. 2024

Sensors

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“…The accuracy of gaze estimation methods largely depends on the effectiveness of feature detection and extraction [196,197]. Appearance-based gaze estimation using deep learning has recently demonstrated robustness against image input noise, blur, and localization errors [191,198]. Several works have primarily focused on appearance-based methods using CNN within the past five years.…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

Remote Eye Gaze Tracking Research: A Comparative Evaluation on Past and Recent Progress

Shehu

Athuman

2021

Electronics

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Several decades of eye related research has shown how valuable eye gaze data are for applications that are essential to human daily life. Eye gaze data in a broad sense has been used in research and systems for eye movements, eye tracking, and eye gaze tracking. Since early 2000, eye gaze tracking systems have emerged as interactive gaze-based systems that could be remotely deployed and operated, known as remote eye gaze tracking (REGT) systems. The drop point of visual attention known as point of gaze (PoG), and the direction of visual attention known as line of sight (LoS), are important tasks of REGT systems. In this paper, we present a comparative evaluation of REGT systems intended for the PoG and LoS estimation tasks regarding past to recent progress. Our literature evaluation presents promising insights on key concepts and changes recorded over time in hardware setup, software process, application, and deployment of REGT systems. In addition, we present current issues in REGT research for future attempts.

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