Get a grip

Santini, Thiago; Kasneci, Enkelejda

doi:10.1145/3314111.3319835

Cited by 24 publications

(5 citation statements)

References 57 publications

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“…Grip This setup used the aforementioned tablet and Pupil headset in combination with the open-source eye-tracking software EyeRecToo (version 2.0 commit 51a839aa). The EyeRecToo platform was used because it includes the following eye-tracking algorithms: Grip (Santini et al, 2019) for gaze estimation, which we chose because it is claimed to be slippage-robust, and PuReST (Santini et al, 2018b) for pupil tracking, which we chose because it is currently the top-performing pupil-tracking algorithm.…”

Section: Apparatusmentioning

confidence: 99%

“…During recording, gaze estimation was achieved using a bivariate polynomial regression (option POLY X Y XY XX YY XYY YXX XXYY in the EyeRec-Too software). The recordings were later post-processed offline with Grip for gaze estimation (Santini et al, 2019), a method that was not yet available at the time of the data recording. Nevertheless, the resulting gaze estimation would be identical if Grip had been run in real-time during recording.…”

Section: Apparatusmentioning

confidence: 99%

“…Lastly, Grip showed the highest sample-to-sample deviation and spatial dispersion levels, which were lowest for the top-left gaze target location and highest for the bottom-right gaze target location, which, respectively, coincided with participant lines of sight furthest away from and closest to the eye tracker's eye camera location. This may have arisen because Grip's gaze estimation algorithm involves determining the 3D slant of the pupil with respect the eye camera, and variability resulting from this procedure becomes larger the more frontoparallel the pupil is to the eye camera (Santini et al, 2019).…”

Section: Nine-point Validation Conditionsmentioning

confidence: 99%

“…As an example of such bespoke eye-tracking setups, we include a fourth setup that also uses the Pupil headset, but replaces the Pupil Capture software with the open-source EyeRecToo software (Santini, Fuhl, Geisler, & Kasneci, 2017). EyeRecToo was chosen for this setup because (1) it is readily available as a pre-compiled executable, (2) previous studies suggest that its tracking algorithms are state-of-the-art (Santini, Fuhl, & Kasneci, 2018b), and (3) it provides the Grip (Santini, Niehorster, & Kasneci, 2019) gaze estimation method, which is claimed to be slippage-robust. We will henceforth refer to this setup as Grip.…”

Section: Introductionmentioning

confidence: 99%

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The impact of slippage on the data quality of head-worn eye trackers

et al. 2020

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Mobile head-worn eye trackers allow researchers to record eye-movement data as participants freely move around and interact with their surroundings. However, participant behavior may cause the eye tracker to slip on the participant's head, potentially strongly affecting data quality. To investigate how this eye-tracker slippage affects data quality, we designed experiments in which participants mimic behaviors that can cause a mobile eye tracker to move. Specifically, we investigated data quality when participants speak, make facial expressions, and move the eye tracker. Four head-worn eye-tracking setups were used: (i) Tobii Pro Glasses 2 in 50 Hz mode, (ii) SMI Eye Tracking Glasses 2.0 60 Hz, (iii) Pupil-Labs' Pupil in 3D mode, and (iv) Pupil-Labs' Pupil with the Grip gaze estimation algorithm as implemented in the EyeRecToo software. Our results show that whereas gaze estimates of the Tobii and Grip remained stable when the eye tracker moved, the other systems exhibited significant errors (0.8-3.1 • increase in gaze deviation over baseline) even for the small amounts of glasses movement that occurred during the speech and facial expressions tasks. We conclude that some of the tested eye-tracking setups may not be suitable for investigating gaze behavior when high accuracy is required, such as during face-to-face interaction scenarios. We recommend that users of mobile head-worn eye trackers perform similar tests with their setups to become aware of its characteristics. This will enable researchers to design experiments that are robust to the limitations of their particular eye-tracking setup. Keywords Head-mounted eye tracking • Wearable eye tracking • Mobile eye tracking • Eye movements • Natural behavior • Data quality Diederick C. Niehorster and Thiago Santini contributed equally and should be considered co-first authors.

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Section: Apparatusmentioning

confidence: 99%

Section: Apparatusmentioning

confidence: 99%

Section: Nine-point Validation Conditionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The impact of slippage on the data quality of head-worn eye trackers

et al. 2020

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“…Horizontal and vertical angles between the optical axis and the line from the centre of the eyeball to the eye camera are simulated and enriched with controlled patterns of measurement noise. We use these gaze angles because they have proven to be relatively stable in terms of device slippage (Santini et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Testing different function fitting methods for mobile eye-tracker calibration

Severitt,

Kübler,

Kasneci

2023

JEMR

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During calibration, an eye-tracker fits a mapping function from features to a target gaze point. While there is research on which mapping function to use, little is known about how to best estimate the function's parameters. We investigate how different fitting methods impact accuracy under different noise factors, such as mobile eye-tracker imprecision or detection errors in feature extraction during calibration. For this purpose, a simulation of binocular gaze was developed for a) different calibration patterns and b) different noise characteristics. We found the commonly used polynomial regression via least-squares-error fit often lacks to find good mapping functions when compared to ridge regression. Especially as data becomes noisier, outlier-tolerant fitting methods are of importance. We demonstrate a reduction in mean MSE of 20% by simply using ridge over polynomial fit in a mobile eye-tracking experiment.

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Visual perception of natural colours in paintings: An eye‐tracking study of Grünewald's Resurrection

Fontoura

2021

Color Research & Application

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This article aims to reveal aspects of the visual perception of the natural colours of Matthias Grünewald's Resurrection panel, part of the Isenheim Altarpiece (1512‐1516). To identify, which colours are mostly perceived, we have performed an eye‐tracking study with 52 participants at the Unterlinden Museum, in Colmar, France, in which we recorded the participant's eye movements and their spontaneous comments when looking at Grünewald's work, using an innovative approach to let visitors be free while recording their gaze in a realistic observation condition. Results indicate that although orange and red colours are perceived faster, yellows and browns are focused for a more extended period and visited more frequently. The Resurrection's colours are perceived by participants as containing strong emotional content, being explicitly crucial for the full understanding of the painting. Yellow remains the most salient colour of the artwork for the observers, and it is associated with light and transcendent content.

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Get a grip

Cited by 24 publications

References 57 publications

The impact of slippage on the data quality of head-worn eye trackers

The impact of slippage on the data quality of head-worn eye trackers

Testing different function fitting methods for mobile eye-tracker calibration

Visual perception of natural colours in paintings: An eye‐tracking study of Grünewald's Resurrection

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