An Eye-Tracking System based on Inner Corner-Pupil Center Vector and Deep Neural Network

Su, Mu Chun; Tat-Meng, U.; Hsieh, Yi-Zeng; Yeh, Zhe Fu; Lee, Shu Fang; Lin, Shih Syun

doi:10.3390/s20010025

Cited by 9 publications

(7 citation statements)

References 16 publications

(14 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Su et al (2019) proposed an eye control system that combines the inner corner-pupil center vector with neural networks. They thought that using a lightweight CNN and vector transformation around the pupil of the eyeball, the eye control system might be implemented in a free environment.…”

Section: Application Of Convolutional Neural Network In Eye Control S...mentioning

confidence: 99%

“…This Gaussian mixture can be interpreted as the network's number of choices for the following raw data sample, given all the inputs so far. Su et al (2019) proposed an eye control system that combines the inner corner-pupil center vector with neural networks. They thought that using a lightweight CNN and vector transformation around the pupil of the eyeball, the eye control system might be implemented in a free environment.…”

Section: Gaze Estimation Based On Monocular or Binocularmentioning

confidence: 99%

See 1 more Smart Citation

Eye control system based on convolutional neural network: a review

Xiong

Nie

2022

View full text Add to dashboard Cite

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.

show abstract

Section: Application Of Convolutional Neural Network In Eye Control S...mentioning

confidence: 99%

Section: Gaze Estimation Based On Monocular or Binocularmentioning

confidence: 99%

Eye control system based on convolutional neural network: a review

Xiong

Nie

2022

View full text Add to dashboard Cite

show abstract

“…pupil/iris, corneal reflection positions) 2 and head motion/pose related features (e.g. eye corner, rigid facial features positions) 4 , 5 which are represented in a global coordinate system. However, representing features in a global coordinate system makes the mapping dependent on absolute values, which can vary significantly across individuals.…”

Section: Introductionmentioning

confidence: 99%

Instant interaction driven adaptive gaze control interface

Qian,

Arichi,

Edwards

et al. 2024

Sci Rep

View full text Add to dashboard Cite

Gaze estimation is long been recognised as having potential as the basis for human-computer interaction (HCI) systems, but usability and robustness of performance remain challenging . This work focuses on systems in which there is a live video stream showing enough of the subjects face to track eye movements and some means to infer gaze location from detected eye features. Currently, systems generally require some form of calibration or set-up procedure at the start of each user session. Here we explore some simple strategies for enabling gaze based HCI to operate immediately and robustly without any explicit set-up tasks. We explore different choices of coordinate origin for combining extracted features from multiple subjects and the replacement of subject specific calibration by system initiation based on prior models. Results show that referencing all extracted features to local coordinate origins determined by subject start position enables robust immediate operation. Combining this approach with an adaptive gaze estimation model using an interactive user interface enables continuous operation with the 75th percentile gaze errors of 0.7$$^\circ $$ ∘ , and maximum gaze errors of 1.7$$^\circ $$ ∘ during prospective testing. There constitute state-of-the-art results and have the potential to enable a new generation of reliable gaze based HCI systems.

show abstract

“…pupil/iris, corneal reflection positions) 2 and head motion/pose related features (e.g. eye corner, rigid facial features positions) 3 , 4 which are represented in a global coordinate system. However, representing features in a global coordinate system makes the mapping dependent on absolute values, which can vary significantly across individuals.…”

Section: Introductionmentioning

confidence: 99%

Instant Gaze (IG): An Interaction Driven Adaptive Gaze Control Interface

Qian,

Arichi,

Edwards

et al. 2023

Preprint

View full text Add to dashboard Cite

Gaze estimation is long been recognised as having potential as the basis for human-computer interaction (HCI) systems, but usability and robustness of performance remain challenging . This work focuses on systems in which there is a live video stream showing enough of the subjects face to track eye movements and some means to infer gaze location from detected eye features. Currently, systems generally require some form of calibration or set-up procedure at the start of each user session. Here we explore some simple strategies for enabling gaze based HCI to operate immediately and robustly without any explicit set-up tasks. We explore different choices of coordinate origin for combining extracted features from multiple subjects and the replacement of subject specific calibration by system initiation based on prior models. Results show that referencing all extracted features to local coordinate origins determined by subject start position enables robust immediate operation. Combining this approach with an adaptive gaze estimation model using an interactive user interface enables continuous operation with the 75th percentile gaze errors of 0.7 degrees, and maximum gaze errors of 1.7 degrees during prospective testing. There constitute state-of-the-art results and have the potential to enable a new generation of reliable gaze based HCI systems.

show abstract

An Eye-Tracking System based on Inner Corner-Pupil Center Vector and Deep Neural Network

Cited by 9 publications

References 16 publications

Eye control system based on convolutional neural network: a review

Eye control system based on convolutional neural network: a review

Instant interaction driven adaptive gaze control interface

Instant Gaze (IG): An Interaction Driven Adaptive Gaze Control Interface

Contact Info

Product

Resources

About