Pupil-Contour-Based Gaze Estimation With Real Pupil Axes for Head-Mounted Eye Tracking

Wan, Zhonghua; Chen, Xiong; Chen, Wenbin; Zhang, Hanyuan; Wu, Shiqian

doi:10.1109/tii.2021.3118022

Cited by 14 publications

(8 citation statements)

References 24 publications

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“…We evaluated the following five methods: (1) Explicit multi-point calibration: the OA vectors and VA vectors corresponding to nine screen points were used to calibrate a transformation matrix for converting the OA to the VA of the eyeball (Zhu and Ji, 2008 ; Wan et al, 2022 ); (2) Explicit single-point calibration: Due to the fact that the kappa angle is an eye invariant parameter, each screen point corresponds to an equal kappa angle. That is to say, any screen point can be selected as the calibration point.…”

Section: Performance Simulations Of Typical Personal Calibration Methodsmentioning

confidence: 99%

A review on personal calibration issues for video-oculographic-based gaze tracking

Liu,

Chi,

Yang

2024

Front. Psychol.

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Personal calibration is a process of obtaining personal gaze-related information by focusing on some calibration benchmarks when the user initially uses a gaze tracking system. It not only provides conditions for gaze estimation, but also improves gaze tracking performance. Existing eye-tracking products often require users to conduct explicit personal calibration first, thereby tracking and interacting based on their gaze. This calibration mode has certain limitations, and there is still a significant gap between theoretical personal calibration methods and their practicality. Therefore, this paper reviews the issues of personal calibration for video-oculographic-based gaze tracking. The personal calibration information in typical gaze tracking methods is first summarized, and then some main settings in existing personal calibration processes are analyzed. Several personal calibration modes are discussed and compared subsequently. The performance of typical personal calibration methods for 2D and 3D gaze tracking is quantitatively compared through simulation experiments, highlighting the characteristics of different personal calibration settings. On this basis, we discuss several key issues in designing personal calibration. To the best of our knowledge, this is the first review on personal calibration issues for video-oculographic-based gaze tracking. It aims to provide a comprehensive overview of the research status of personal calibration, explore its main directions for further studies, and provide guidance for seeking personal calibration modes that conform to natural human-computer interaction and promoting the widespread application of eye-movement interaction.

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Section: Performance Simulations Of Typical Personal Calibration Methodsmentioning

confidence: 99%

A review on personal calibration issues for video-oculographic-based gaze tracking

Liu,

Chi,

Yang

2024

Front. Psychol.

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“…Studies have shown that human visual attention can express the mental processes revealed by gaze cues, which are associated with visual objects [10]- [12]. Previous work has studied eye-tracking technology to estimate the gaze point in the scene [13] and designed an auxiliary robot to help weak functional groups such as ALS [14]. These patients do not have full abilities of muscle movements but still retain the ability to control eye movement.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Method for Implicit Intention Inference Based on Punished-Weighted Naïve Bayes

Gao

Wan

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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Gaze-based implicit intention inference provides a new human-robot interaction for people with disabilities to accomplish activities of daily living independently. Existing gaze-based intention inference is mainly implemented by the data-driven method without prior object information in intention expression, which yields low inference accuracy. Aiming to improve the inference accuracy, we propose a gaze-based hybrid method by integrating model-driven and data-driven intention inference tailored to disability applications. Specifically, intention is considered as the combination of verbs and nouns. The objects corresponding to the nouns are regarded as intention-interpreting objects and served as prior knowledge, i.e., punished factors. The punished factor considers the object information, i.e., the priority in object selection. Class-specific attribute weighted naïve Bayes model learned through training data is presented to represent the relationship among intentions and objects. An intention inference engine is developed by combining the human prior knowledge, and the data-driven classspecific attribute weighted naïve Bayes model. Computer simulations: (i) verify the contribution of each critical component of the proposed model, (ii) evaluate the inference accuracy of the proposed model, and (iii) show that the proposed method is superior to state-of-the-art intention inference methods in terms of accuracy.

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“…Eye-tracking is an emerging technology for users' gaze point estimation [7]. Even in severe hemiplegia and other motor disorders, the human oculomotor system typically remains intact [8], [9], making eye-tracking accessible to users with disabilities.…”

Section: Introductionmentioning

confidence: 99%

Gaze and Environmental Context-Guided Deep Neural Network and Sequential Decision Fusion for Grasp Intention Recognition

Yang,

Chen,

Xiao

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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Grasp intention recognition plays a crucial role in controlling assistive robots to aid older people and individuals with limited mobility in restoring arm and hand function. Among the various modalities used for intention recognition, the eye-gaze movement has emerged as a promising approach due to its simplicity, intuitiveness, and effectiveness. Existing gaze-based approaches insufficiently integrate gaze data with environmental context and underuse temporal information, leading to inadequate intention recognition performance. The objective of this study is to eliminate the proposed deficiency and establish a gaze-based framework for object detection and its associated intention recognition. A novel gaze-based grasp intention recognition and sequential decision fusion framework (GIRSDF) is proposed. The GIRSDF comprises three main components: gaze attention map generation, the Gaze-YOLO grasp intention recognition model, and sequential decision fusion models (HMM, LSTM, and GRU). To evaluate the performance of GIRSDF, a dataset named Invisible containing data from healthy individuals and hemiplegic patients is established. GIRSDF is validated by trial-based and subject-based experiments on Invisible and outperforms the previous gaze-based grasp intention recognition methods. In terms of running efficiency, the proposed framework can run at a frequency of about 22 Hz, which ensures real-time grasp intention recognition. This study is expected to inspire additional gaze-related grasp intention recognition works.

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Pupil-Contour-Based Gaze Estimation With Real Pupil Axes for Head-Mounted Eye Tracking

Cited by 14 publications

References 24 publications

A review on personal calibration issues for video-oculographic-based gaze tracking

A review on personal calibration issues for video-oculographic-based gaze tracking

A Hybrid Method for Implicit Intention Inference Based on Punished-Weighted Naïve Bayes

Gaze and Environmental Context-Guided Deep Neural Network and Sequential Decision Fusion for Grasp Intention Recognition

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