Appearance-Based Gaze Tracking: A Brief Review

Jiang, Jiaqi; Zhou, Xiaolong; Chan, Sixian; Chen, Shengyong

doi:10.1007/978-3-030-27529-7_53

Cited by 14 publications

(8 citation statements)

References 25 publications

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“…Although the error is small, it is too dependent on the light source and has a cumbersome calibration process. The appearance-based method [7] mainly uses human eyes or facial images as input, establishes a mapping model between features and gaze direction, and outputs the gaze direction. This method is simple in design, low in cost, and highly robust.…”

Section: A Based On Non-invasive Appearance Gaze Tracking Technologymentioning

confidence: 99%

“…This study aims to introduce a non-invasive appearancebased tracking technique [7] for acquiring eye-tracking data and a novel gaze target area classification method based on a hybrid Swin Transformer to improve the accuracy of gaze target area classification and detection. Hybrid Swin Transformer is a deep learning model that inherits the residual structure of ResNet50 [8] and the local perception capability of Swin Transformer [9], organically combining the two.…”

Section: Introductionmentioning

confidence: 99%

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Hybrid Swin Transformer-Based Classification of Gaze Target Regions

Wu,

Wang,

Gao

et al. 2023

IEEE Access

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Inferring gaze targeting or gaze following is an effective method for understanding human behavior and intentions. To address errors caused by issues such as the disappearance of the eye image and head deflection occlusion in image capture, this paper adopts a non-intrusive appearance-based tracking technique using a binocular stereo vision camera to capture the face image and the head pose. It determines each gaze direction based on a single image frame. Furthermore, to handle head motion and view direction effectively and improve the classification and detection ability of the gaze target region, this paper proposes a hybrid structure for the Swin Transformer gaze target region classification method. Initially, both the ResNet50 model and Swin Transformer model are employed to extract facial image features, and subsequently, head pose features are fused to classify the gaze target area. The paper also compares the classification effects of different structural models. The analysis of the results demonstrates that the hybrid Swin Transformer model is more effective in classifying and detecting the gaze target region, achieving an accuracy rate of 90%. Lastly, the paper analyzes the gaze of flight trainees during flight missions using a heatmap, which serves as a foundation for subsequent analyses of pilot attention and operational intentions during flights.INDEX TERMS Gaze estimation, swin transformer, computer vision, region classification.

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Section: A Based On Non-invasive Appearance Gaze Tracking Technologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hybrid Swin Transformer-Based Classification of Gaze Target Regions

Wu,

Wang,

Gao

et al. 2023

IEEE Access

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“…Gaze tracking are the techniques that allows estimate the direction where an person is looking at [21] This problem, generally, can be faced through two different methods: appearance-based methods and model-based methods [22].…”

Section: Related Researchmentioning

confidence: 99%

“…The mapping function is usually learned using different machine learning algorithms such as K-Nearest Neighbor [23][24][25], Random Forest [26,27], Support Vector Machines [28] or Artificial Neural Networks [29][30][31][32]. A current review of these methods can be found in [22,33]. Although appearance-based methods do not require any knowledge about human vision and they only need simple eye detection techniques, these methods require a large amount of data to learn the mapping function and consider only the gaze estimation problem in 2D, and this is an essential limitation in real problems such as driving.…”

Section: Related Researchmentioning

confidence: 99%

Implementing a Gaze Tracking Algorithm for Improving Advanced Driver Assistance Systems

et al. 2021

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Car accidents are one of the top ten causes of death and are produced mainly by driver distractions. ADAS (Advanced Driver Assistance Systems) can warn the driver of dangerous scenarios, improving road safety, and reducing the number of traffic accidents. However, having a system that is continuously sounding alarms can be overwhelming or confusing or both, and can be counterproductive. Using the driver’s attention to build an efficient ADAS is the main contribution of this work. To obtain this “attention value” the use of a Gaze tracking is proposed. Driver’s gaze direction is a crucial factor in understanding fatal distractions, as well as discerning when it is necessary to warn the driver about risks on the road. In this paper, a real-time gaze tracking system is proposed as part of the development of an ADAS that obtains and communicates the driver’s gaze information. The developed ADAS uses gaze information to determine if the drivers are looking to the road with their full attention. This work gives a step ahead in the ADAS based on the driver, building an ADAS that warns the driver only in case of distraction. The gaze tracking system was implemented as a model-based system using a Kinect v2.0 sensor and was adjusted on a set-up environment and tested on a suitable-features driving simulation environment. The average obtained results are promising, having hit ratios between 96.37% and 81.84%.

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“…Data-driven appearance-based gaze estimation has proven to be a feasible alternative to model-based methods, especially in remote scenarios where lower eye-image resolution does not allow to create a robust model of the eye, and in setups where glints are not available [3]. During the last decade, deep learning based solutions for gaze estimation have started to emerge due to their excellent performance on a wide range of applications [9], [5]. Such approaches are usually posed as a regression problem, using Convolutional Neural Networks (CNNs) to extract static features from eye-region [13] or whole-face images [14] to estimate the direction of gaze.…”

Section: Introductionmentioning

confidence: 99%

Benefits of temporal information for appearance-based gaze estimation

Palmero,

Komogortsev,

Talathi

2020

Preprint

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State-of-the-art appearance-based gaze estimation methods, usually based on deep learning techniques, mainly rely on static features. However, temporal trace of eye gaze contains useful information for estimating a given gaze point. For example, approaches leveraging sequential eye gaze information when applied to remote or low-resolution image scenarios with off-the-shelf cameras are showing promising results. The magnitude of contribution from temporal gaze trace is yet unclear for higher resolution/frame rate imaging systems, in which more detailed information about an eye is captured. In this paper, we investigate whether temporal sequences of eye images, captured using a high-resolution, high-frame rate head-mounted virtual reality system, can be leveraged to enhance the accuracy of an end-to-end appearance-based deep-learning model for gaze estimation. Performance is compared against a staticonly version of the model. Results demonstrate statisticallysignificant benefits of temporal information, particularly for the vertical component of gaze.

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Appearance-Based Gaze Tracking: A Brief Review

Cited by 14 publications

References 25 publications

Hybrid Swin Transformer-Based Classification of Gaze Target Regions

Hybrid Swin Transformer-Based Classification of Gaze Target Regions

Implementing a Gaze Tracking Algorithm for Improving Advanced Driver Assistance Systems

Benefits of temporal information for appearance-based gaze estimation

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