ExCuSe: Robust Pupil Detection in Real-World Scenarios

Fuhl, Wolfgang; Kübler, Thomas C.; Sippel, Katrin; Rosenstiel, Wolfgang; Kasneci, Enkelejda

doi:10.1007/978-3-319-23192-1_4

Cited by 162 publications

(184 citation statements)

References 17 publications

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“…For this conversion, we multiplied pixel size of our monitor (0.29 mm) with the errors E dx and E dy . Then the x-axis, y-axis and Euclidean errors in degrees are calculated with Equations (19)- (21).…”

Section: Resultsmentioning

confidence: 99%

Design of a Binocular Pupil and Gaze Point Detection System Utilizing High Definition Images

Durna

Arı

2017

Applied Sciences

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This study proposes a novel binocular pupil and gaze detection system utilizing a remote full high definition (full HD) camera and employing LabVIEW. LabVIEW is inherently parallel and has fewer time-consuming algorithms. Many eye tracker applications are monocular and use low resolution cameras due to real-time image processing difficulties. We utilized the computer's direct access memory channel for rapid data transmission and processed full HD images with LabVIEW. Full HD images make easier determinations of center coordinates/sizes of pupil and corneal reflection. We modified the camera so that the camera sensor passed only infrared (IR) images. Glints were taken as reference points for region of interest (ROI) area selection of the eye region in the face image. A morphologic filter was applied for erosion of noise, and a weighted average technique was used for center detection. To test system accuracy with 11 participants, we produced a visual stimulus set up to analyze each eye's movement. Nonlinear mapping function was utilized for gaze estimation. Pupil size, pupil position, glint position and gaze point coordinates were obtained with free natural head movements in our system. This system also works at 2046 × 1086 resolution at 40 frames per second. It is assumed that 280 frames per second for 640 × 480 pixel images is the case. Experimental results show that the average gaze detection error for 11 participants was 0.76 • for the left eye, 0.89 • for right eye and 0.83 • for the mean of two eyes.

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Section: Resultsmentioning

confidence: 99%

Design of a Binocular Pupil and Gaze Point Detection System Utilizing High Definition Images

Durna

Arı

2017

Applied Sciences

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“…Wood et al [37] presented a model-based gaze estimation system for unmodified tablet computers. Three recent methods, SET [8], ExCuSe [5], and ElSe [6], explicitly address the aforementioned challenges associated with pupil detection in natural environments. Three recent methods, SET [8], ExCuSe [5], and ElSe [6], explicitly address the aforementioned challenges associated with pupil detection in natural environments.…”

Section: State-of-the-art Methods For Pupil Detectionmentioning

confidence: 99%

“…Their gaze estimation pipeline also includes a Haar-like feature based eye detector as well as a RANSAC-based limbus ellipse fitting approach. ExCuSe [5] first analyses the input images with regard to reflections based on intensity histograms. SET is based on thresholding and ellipse fitting.…”

Section: State-of-the-art Methods For Pupil Detectionmentioning

confidence: 99%

“…Three recent methods, SET [8], ExCuSe [5], and ElSe [6], explicitly address the aforementioned challenges associated with pupil detection in natural environments. Similar to ExCuSe, ElSe is also based on edge filtering, ellipse evaluation, and pupil contour validation [6]. ExCuSe [5] first analyses the input images with regard to reflections based on intensity histograms.…”

Section: State-of-the-art Methods For Pupil Detectionmentioning

confidence: 99%

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

Fuhl

Tonsen

Bulling

et al. 2016

Machine Vision and Applications

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134

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Robust and accurate detection of the pupil position is a key building block for head-mounted eye tracking and prerequisite for applications on top, such as gaze-based human-computer interaction or attention analysis. Despite a large body of work, detecting the pupil in images recorded under real-world conditions is challenging given significant variability in the eye appearance (e.g., illumination, reflections, occlusions, etc.), individual differences in eye physiology, as well as other sources of noise, such as contact lenses or make-up. In this paper we review six state-of-the-art pupil detection methods, namely ElSe (Fuhl et al. in Proceed-

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“…A more recent dataset was introduced in [W. Fuhl 2015]. The dataset is significantly larger than the first dataset and images were recorded with a head-mounted eye tracker in uncontrolled environments, namely while driving and going shopping, but not in fully outdoor environments.…”

Section: Introductionmentioning

confidence: 99%

Labelled pupils in the wild

Tonsen

Zhang

Sugano

et al. 2016

Proceedings of the Ninth Biennial ACM Symposium on Eye Tracking Research &Amp; Applications

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We present labelled pupils in the wild (LPW), a novel dataset of 66 high-quality, high-speed eye region videos for the development and evaluation of pupil detection algorithms. The videos in our dataset were recorded from 22 participants in everyday locations at about 95 FPS using a state-of-the-art dark-pupil head-mounted eye tracker. They cover people with different ethnicities, a diverse set of everyday indoor and outdoor illumination environments, as well as natural gaze direction distributions. The dataset also includes participants wearing glasses, contact lenses, as well as make-up. We benchmark five state-of-the-art pupil detection algorithms on our dataset with respect to robustness and accuracy. We further study the influence of image resolution, vision aids, as well as recording location (indoor, outdoor) on pupil detection performance. Our evaluations provide valuable insights into the general pupil detection problem and allow us to identify key challenges for robust pupil detection on head-mounted eye trackers.

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ExCuSe: Robust Pupil Detection in Real-World Scenarios

Cited by 162 publications

References 17 publications

Design of a Binocular Pupil and Gaze Point Detection System Utilizing High Definition Images

Design of a Binocular Pupil and Gaze Point Detection System Utilizing High Definition Images

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

Labelled pupils in the wild

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