Pupil detection for head-mounted eye tracking in the wild: an evaluation of the state of the art

Fuhl, Wolfgang; Tonsen, Marc; Bulling, Andreas; Kasneci, Enkelejda

doi:10.1007/s00138-016-0776-4

Cited by 136 publications

(76 citation statements)

References 34 publications

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“…This is in stark contrast to remote eye tracking, where several studies have called attention to the characterization of eye-tracking data quality (Blignaut & Wium 2014;Wass, Forssman, & Leppänen, 2014;Nyström, Andersson, Holmqvist, & van de Weijer, 2013;Hessels, Andersson, Hooge, Nyström, & Kemner, 2015), with some studies specifically examining data quality using a series of tests mimicking participant behavior during typical recording sessions (Hessels, Cornelissen, Kemner, & Hooge, 2015;Niehorster, Cornelissen, Holmqvist, Hooge, & Hessels, 2018). Although it has been established that eye camera positioning and illumination conditions can greatly influence tracking quality in head-worn eye tracking (Świrski, Bülling, & Dodgson, 2012;Tonsen, Zhang, Sugano, & Bülling, 2016;Fuhl, Tonsen, Bülling, & Kasneci, 2016), to the best of the authors' knowledge, only a single study has actually empirically compared the accuracy and precision of multiple head-worn eyetracking setups-yet the study (MacInnes, Iqbal, Pearson, & Johnson, 2018) was limited in scope to the ideal case of careful calibration and evaluation immediately thereafter. It is therefore not representative of how these eye-tracking setups are often used with unconstrained participants in uncontrolled environments.…”

Section: Introductionmentioning

confidence: 99%

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: Introductionmentioning

confidence: 99%

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

et al. 2020

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“…Short trial durations and repeated stimulus display in a laboratory setting is likely to lead to highly similar scanpaths and few noise. On the other hand, real-world experiments are always associated with a high level of pupil detection failures (a review can be found in Fuhl et al (2016)) and identical experimental conditions are hard to reproduce (e.g., the same amount traffic while driving). Scanpaths of such experiments are likely to be more dissimilar and noisy.…”

Section: Experimental Settings and Datamentioning

confidence: 99%

SubsMatch 2.0: Scanpath comparison and classification based on subsequence frequencies

et al. 2016

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Our eye movements are driven by a continuous trade-off between the need for detailed examination of objects of interest and the necessity to keep an overview of our surrounding. In consequence, behavioral patterns that are characteristic for our actions and their planning are typically manifested in the way we move our eyes to interact with our environment. Identifying such patterns from individual eye movement measurements is however highly challenging. In this work, we tackle the challenge of quantifying the influence of experimental factors on eye movement sequences. We introduce an algorithm for extracting sequence-sensitive features from eye movements and for the classification of eye movements based on the frequencies of small subsequences. Our approach is evaluated against the state-of-the art on a novel and a very rich collection of eye movements data derived from four experimental settings, from static viewing tasks to highly dynamic outdoor settings. Our results show that the proposed method is able to classify eye movement sequences over a variety of experimental designs. The choice of parameters is discussed in detail with special focus on highlighting different aspects of general scanpath shape. Algorithms and evaluation data are available at: www.ti.uni-tuebingen.de/scanpathcomparison.html.

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“…Therefore, by using a visible light, we also explore a human eye-computer interaction system. Recently, pupil detection methods are evaluated [10]. In the future, we investigate methods described in the paper [10].…”

Section: Resultsmentioning

confidence: 99%

Pupil Detection and Tracking Based on a Round Shape Criterion by Image Pocessing Techniques for a Human Eye-Computer Interaction system

Ochiai¹,

Mitani²

2017

IJCTE

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The pupil detection and tracking is one of important steps for developing a human-computer interaction system. To develop a human eye-computer interaction system, we study pupil detection and tracking by image processing techniques. In the image processing, the illumination directly influences the image quality in general. If influence of illumination is little, we can obtain an image of good image quality. Therefore, we proposed a hardware constitution of an infrared light-emitting diode (LED) light, a sensitive infrared camera, and an infrared (IR) filter for pupil detection and tracking, in order to avoid the influence of illumination. However, detecting a pupil was still poor because only thresholding was carried out in a pupil image. The previously proposed method has no consideration of a round shape criterion to detect a pupil part. In this paper, in order to detect a pupil more accurately, we have proposed to use a round shape criterion of a pupil. In the experiment, we investigate the robustness of pupil detection and tracking by image processing techniques for a human eye-computer interaction system. IndexTerms-Human-computer interaction, image processing techniques, pupil detection and tracking, round shape criterion, infrared filter.

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Pupil detection for head-mounted eye tracking in the wild: an evaluation of the state of the art

Cited by 136 publications

References 34 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

SubsMatch 2.0: Scanpath comparison and classification based on subsequence frequencies

Pupil Detection and Tracking Based on a Round Shape Criterion by Image Pocessing Techniques for a Human Eye-Computer Interaction system

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