Prediction of gaze estimation error for error-aware gaze-based interfaces

Barz, Michael; Daiber, Florian; Bulling, Andreas

doi:10.1145/2857491.2857493

Cited by 25 publications

(21 citation statements)

References 15 publications

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“…We share the vision of other researchers (e.g. [2,3,36]) that applications should be aware of uncertainty in the input signal and adapt their interface and functionality accordingly. However, eye tracking poses a challenge in that the error of the estimated gaze point varies substantially depending on the interplay of many factors and thus is hard to predict and correct.…”

Section: Error-aware and Adaptive Applicationsmentioning

confidence: 94%

“…prior work [39] by evaluating several real-time filtering techniques for interactive applications based on our collected data, and propose an approach to choose optimal parameters. Recent efforts model and predict the error when estimating the gaze position on head-worn eye tracking glasses [2,3]. Such models are the first step towards adaptive and personalized user interfaces for gaze interaction that adapt to the quality of the tracked gaze.…”

Section: Algorithmic Approaches To Robust Interactionmentioning

confidence: 99%

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Toward Everyday Gaze Input

Feit

Williams

Toledo

et al. 2017

Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems

166

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For eye tracking to become a ubiquitous part of our everyday interaction with computers, we first need to understand its limitations outside rigorously controlled labs, and develop robust applications that can be used by a broad range of users and in various environments. Toward this end, we collected eye tracking data from 80 people in a calibration-style task, using two different trackers in two lighting conditions. We found that accuracy and precision can vary between users and targets more than six-fold, and report on differences between lighting, trackers, and screen regions. We show how such data can be used to determine appropriate target sizes and to optimize the parameters of commonly used filters. We conclude with design recommendations and examples how our findings and methodology can inform the design of erroraware adaptive applications.

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Section: Error-aware and Adaptive Applicationsmentioning

confidence: 94%

Section: Algorithmic Approaches To Robust Interactionmentioning

confidence: 99%

Toward Everyday Gaze Input

Feit

Williams

Toledo

et al. 2017

Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems

166

View full text Add to dashboard Cite

show abstract

“…The gaze position estimated by an eye tracker will be affected by different sources of error, such as measurement errors, or limitations of the model [3]. Even if the other sources of error were minimized, the size of the fovea limits the accuracy of the estimated gaze location to about 0.5 degrees of visual angle [22].…”

Section: Eye Tracker and The Gaze Positionmentioning

confidence: 99%

EyeSwipe: text entry using gaze paths

Kurauchi¹

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AcknowledgmentsCountless people contributed directly or indirectly to this thesis. First and foremost I thank God, creator and sustainer of life. To him be the glory. People with severe motor disabilities may communicate using their eye movements aided by a virtual keyboard and an eye tracker. Text entry by gaze may also benefit users immersed in virtual or augmented realities, when they do not have access to a physical keyboard or touchscreen. Thus, both users with and without disabilities may take advantage of the ability to enter text by gaze.However, methods for text entry by gaze are typically slow and uncomfortable. In this thesis we propose EyeSwipe as a step further towards fast and comfortable text entry by gaze.EyeSwipe maps gaze paths into words, similarly to how finger traces are used on swipe-based methods for touchscreen devices. A gaze path differs from the finger trace in that it does not have clear start and end positions. To segment the gaze path from the user's continuous gaze data stream, EyeSwipe requires the user to explicitly indicate its beginning and end. The user can quickly glance at the vicinity of the other characters that compose the word. Candidate words are sorted based on the gaze path and presented to the user.We discuss two versions of EyeSwipe. EyeSwipe 1 uses a deterministic gaze gesture called Reverse Crossing to select both the first and last letters of the word. Considering the lessons learned during the development and test of EyeSwipe 1 we proposed EyeSwipe 2. The user emits commands to the interface by switching the focus between regions.In a text entry experiment comparing EyeSwipe 2 to EyeSwipe 1, 11 participants achieved an average text entry rate of 12.58 words per minute (wpm) with EyeSwipe 1 and 14.59 wpm with EyeSwipe 2 after using each method for 75 minutes. The maximum entry rates achieved with EyeSwipe 1 and EyeSwipe 2 were, respectively, 21.27 wpm and 32.96 wpm. Participants considered EyeSwipe 2 to be more comfortable and faster, while less accurate than EyeSwipe 1. Additionally, with EyeSwipe 2 we proposed the use of gaze path data to dynamically adjust the gaze estimation.Using data from the experiment we show that gaze paths can be used to dynamically improve gaze estimation during the interaction. Discutimos duas versões do EyeSwipe. O EyeSwipe 1 usa um gesto do olhar determinístico chamado Cruzamento Reverso para selecionar tanto a primeira quanto a última letra da palavra.

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“…For example high error values are found to occur near the display corners and near the screen borders which could be due to high visual angles in those regions. Impact of display screen locations where gaze is tracked on corresponding gaze error levels has been reported in [ 61 ] which shows that gaze errors may increase by 33% on the screen border regions compared to those in the central regions.…”

Section: Visualizations For Evaluating Gaze Estimation Systemsmentioning

confidence: 99%

Performance Evaluation Strategies for Eye Gaze Estimation Systems with Quantitative Metrics and Visualizations

Kar

Corcoran

2018

Sensors

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An eye tracker’s accuracy and system behavior play critical roles in determining the reliability and usability of eye gaze data obtained from them. However, in contemporary eye gaze research, there exists a lot of ambiguity in the definitions of gaze estimation accuracy parameters and lack of well-defined methods for evaluating the performance of eye tracking systems. In this paper, a set of fully defined evaluation metrics are therefore developed and presented for complete performance characterization of generic commercial eye trackers, when they operate under varying conditions on desktop or mobile platforms. In addition, some useful visualization methods are implemented, which will help in studying the performance and data quality of eye trackers irrespective of their design principles and application areas. Also the concept of a graphical user interface software named GazeVisual v1.1 is proposed that would integrate all these methods and enable general users to effortlessly access the described metrics, generate visualizations and extract valuable information from their own gaze datasets. We intend to present these tools as open resources in future to the eye gaze research community for use and further advancement, as a contribution towards standardization of gaze research outputs and analysis.

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Prediction of gaze estimation error for error-aware gaze-based interfaces

Cited by 25 publications

References 15 publications

Toward Everyday Gaze Input

Toward Everyday Gaze Input

EyeSwipe: text entry using gaze paths

Performance Evaluation Strategies for Eye Gaze Estimation Systems with Quantitative Metrics and Visualizations

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