Gaze Estimation Method Using Analysis of Electrooculogram Signals and Kinect Sensor

Sakurai, Kouhei; Yan, Mingmin; Tanno, Koichi; Tamura, H.

doi:10.1155/2017/2074752

Cited by 14 publications

(7 citation statements)

References 25 publications

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“…Despite these challenges, researchers have significantly advanced in developing gaze estimation methods specifically designed for mobile devices [44,45]. These methods often leverage machine learning algorithms and utilize built-in sensors, such as front-facing cameras and inertial sensors, to estimate the user's gaze direction [46]. Moreover, techniques such as deep learning-based gaze estimation models have been proposed to provide superior accuracy in predicting gaze direction [28,29].…”

Section: Gaze Estimation On Mobile Devicesmentioning

confidence: 99%

E2GCO: Event-driven Eye-Gaze-Controlled Operation for Smartphone Interaction

Liang,

Yan,

et al. 2024

Preprint

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Existing gaze-based smartphone interaction methods generally need the auxiliary of hand motions to realize control, and most of them can achieve only simple control operations, which are far from practical smartphone control. To this end, we propose a novel eye-gaze-based smartphone interaction method named Event-driven Eye-Gaze-Controlled Operation ($E^2GCO$), which can realize comprehensive smartphone control operations just using human eyes and gazes. We designed four pairs of eye-gaze-based interaction actions that are easy to implement and distinguish, based on which $E^2GCO$ can control all kinds of smartphone pages. Before the interaction, an anti-jitter strategy-based gaze estimation method is exploited to solve the jitter problem of human eye fixation and detect both accurate and stable human gaze locations on smartphone screens, even when the users are moving. The interaction can be executed more accurately based on the stable and accurate gaze locations. In addition, the event camera technology is introduced in $E^2GCO$ to significantly decrease its power consumption, making $E^2GCO$ more power-efficient to deploy on smartphones. We have implemented the prototype of $E^2GCO$ on different brands of smartphones and conducted a comprehensive user study to validate its efficacy. The user study demonstrates $E^2GCO$'s superior smartphone control capabilities across various scenarios.

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Section: Gaze Estimation On Mobile Devicesmentioning

confidence: 99%

E2GCO: Event-driven Eye-Gaze-Controlled Operation for Smartphone Interaction

Liang,

Yan,

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Gaze estimation on mobile devices is much more challenging due to limited computational resources and constrained hardware [44][45][46]. These methods often leverage machine learning algorithms and utilize built-in sensors, such as front-facing cameras and inertial sensors, to estimate the user's gaze direction [47]. Moreover, gaze estimation models have been proposed to provide superior accuracy in predicting gaze direction [29,30].…”

Section: Gaze Estimation On Mobile Devicesmentioning

confidence: 99%

E2GO : Free Your Hands for Smartphone Interaction

Liang,

Yan,

et al. 2024

Preprint

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Current eye-gaze interaction technologies for smartphones are considered inflexible, inaccurate, and power-hungry. These methods typically rely on hand involvement and accomplish partial interactions. In this paper, we propose a novel eye-gaze smartphone interaction method named Event-driven Eye-Gaze Operation (E2GO), which can realize comprehensive interaction using only eyes and gazes to cover various interaction types. Before the interaction, an anti-jitter gaze estimation method was exploited to stabilize human eye fixation and predict accurate and stable human gaze positions on smartphone screens to further explore refined time-dependent eye-gaze interactions. We also integrated an event-triggering mechanism in E2GO to significantly decrease its power consumption to deploy on smartphones. We have implemented the prototype of E2GO on different brands of smartphones and conducted a comprehensive user study to validate its efficacy, demonstrating E2GO’s superior smartphone control capabilities across various scenarios.

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“…Sakurai et al [199] developed an eye-tracking method using a compact and light electrooculogram (EOG) signal. Further, this prototype is improved via the usage of the EOG component which strongly correlated with the change of eye movements [200] (Refer Fig. 9).…”

Section: Eye Gaze In Healthcare and Wellbeingmentioning

confidence: 99%

Automatic Gaze Analysis: A Survey of Deep Learning based Approaches

Ghosh¹,

Dhall²,

Hayat

et al. 2021

Preprint

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Eye gaze analysis is an important research problem in the field of Computer Vision and Human-Computer Interaction. Even with notable progress in the last 10 years, automatic gaze analysis still remains challenging due to the uniqueness of eye appearance, eye-head interplay, occlusion, image quality, and illumination conditions. There are several open questions including what are the important cues to interpret gaze direction in an unconstrained environment without prior knowledge and how to encode them in real-time. We review the progress across a range of gaze analysis tasks and applications to elucidate these fundamental questions; identify effective methods in gaze analysis and provide possible future directions. We analyze recent gaze estimation and segmentation methods, especially in the unsupervised and weakly supervised domain, based on their advantages and reported evaluation metrics. Our analysis shows that the development of a robust and generic gaze analysis method still needs to address real-world challenges such as unconstrained setup and learning with less supervision. We conclude by discussing future research directions for designing a real-world gaze analysis system that can propagate to other domains including Computer Vision, Augmented Reality (AR), Virtual Reality (VR), and Human Computer Interaction (HCI). Project Page: https://github.com/i-am-shreya/EyeGazeSurvey

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Gaze Estimation Method Using Analysis of Electrooculogram Signals and Kinect Sensor

Cited by 14 publications

References 25 publications

E2GCO: Event-driven Eye-Gaze-Controlled Operation for Smartphone Interaction

E2GCO: Event-driven Eye-Gaze-Controlled Operation for Smartphone Interaction

E2GO : Free Your Hands for Smartphone Interaction

Automatic Gaze Analysis: A Survey of Deep Learning based Approaches

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