mlsanityData to 14 Oct 2026

1000 Pupil Segmentations in a Second using Haar Like Features and Statistical Learning

arXiv 2102.01921 ↗First posted 3 Feb 2021eess.IV cs.CVcited by 7

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Abstract (arXiv)

In this paper we present a new approach for pupil segmentation. It can be computed and trained very efficiently, making it ideal for online use for high speed eye trackers as well as for energy saving pupil detection in mobile eye tracking. The approach is inspired by the BORE and CBF algorithms and generalizes the binary comparison by Haar features. Since these features are intrinsically very susceptible to noise and fluctuating light conditions, we combine them with conditional pupil shape probabilities. In addition, we also rank each feature according to its importance in determining the pupil shape. Another advantage of our method is the use of statistical learning, which is very efficient and can even be used online. https://atreus.informatik.uni-tuebingen.de/seafile/d/8e2ab8c3fdd444e1a135/?p=%2FStatsPupil&mode=list

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 25 of its 89 references that are in the map. First posted on arXiv 3 Feb 2021.

Builds on

25 of 89 references.

References · 25

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2018MobileNetV2: Inverted Residuals and Linear Bottleneckscited by 4,795
  3. 2017MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applicationscited by 3,845
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  5. 2016Eye Tracking for Everyonecited by 155
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  9. 2020EllSeg: An Ellipse Segmentation Framework for Robust Gaze Trackingcited by 24
  10. 2016PupilNet: Convolutional Neural Networks for Robust Pupil Detectioncited by 20
  11. 2015Labeled pupils in the wild: A dataset for studying pupil detection in unconstrained environmentscited by 18
  12. 2015ElSe: Ellipse Selection for Robust Pupil Detection in Real-World Environmentscited by 16
13 more
  1. 2017PupilNet v2.0: Convolutional Neural Networks for CPU based real time Robust Pupil Detectioncited by 15
  2. 2020Fully Convolutional Neural Networks for Raw Eye Tracking Data Segmentation, Generation, and Reconstructioncited by 12
  3. 2020Rotated Ring, Radial and Depth Wise Separable Radial Convolutionscited by 12
  4. 2018Eye movement velocity and gaze data generator for evaluation, robustness testing and assess of eye tracking software and visualization toolscited by 11
  5. 2019Learning to Validate the Quality of Detected Landmarkscited by 11
  6. 2020Weight and Gradient Centralization in Deep Neural Networkscited by 11
  7. 2017Fast camera focus estimation for gaze-based focus controlcited by 10
  8. 2018Eye movement simulation and detector creation to reduce laborious parameter adjustmentscited by 10
  9. 2019Training Decision Trees as Replacement for Convolution Layerscited by 10
  10. 2021The Gaze and Mouse Signal as additional Source for User Fingerprints in Browser Applicationscited by 10
  11. 2020Multi Layer Neural Networks as Replacement for Pooling Operationscited by 8
  12. 2020Explainable Online Validation of Machine Learning Models for Practical Applicationscited by 8
  13. 2021A Multimodal Eye Movement Dataset and a Multimodal Eye Movement Segmentation Analysiscited by 7

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Cited by year

Citing papers per year: 2021: 2; 2022: 2; 2023: 0; 2024: 2; 2025: 1.

7 papers.

  1. 2021Maximum and Leaky Maximum Propagationcited by 4
  2. 2021Tensor Normalization and Full Distribution Trainingcited by 3
  3. 2022GroupGazer: A Tool to Compute the Gaze per Participant in Groups with integrated Calibration to Map the Gaze Online to a Screen or Beamer Projectioncited by 2
  4. 2024Towards Unsupervised Eye-Region Segmentation for Eye Trackingcited by 2
  5. 2024Using Deep Learning to Increase Eye-Tracking Robustness, Accuracy, and Precision in Virtual Realitycited by 1
  6. 2022Technical Report: Combining knowledge from Transfer Learning during training and Wide Resnetscited by 0
  7. 2025GazeTrack: High-Precision Eye Tracking Based on Regularization and Spatial Computingcited by 0