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

arXiv: https://arxiv.org/abs/2102.01921 · HTML: https://mlsanity.com/paper/2102.01921/

## 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 (most cited first, 25 of 25)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [Searching for MobileNetV3](https://mlsanity.com/paper/1905.02244/) (2019)
- [Eye Tracking for Everyone](https://mlsanity.com/paper/1606.05814/) (2016)
- [Appearance-Based Gaze Estimation in the Wild](https://mlsanity.com/paper/1504.02863/) (2015)
- [ETH-XGaze: A Large Scale Dataset for Gaze Estimation under Extreme Head Pose and Gaze Variation](https://mlsanity.com/paper/2007.15837/) (2020)
- [RITnet: Real-time Semantic Segmentation of the Eye for Gaze Tracking](https://mlsanity.com/paper/1910.00694/) (2019)
- [EllSeg: An Ellipse Segmentation Framework for Robust Gaze Tracking](https://mlsanity.com/paper/2007.09600/) (2020)
- [PupilNet: Convolutional Neural Networks for Robust Pupil Detection](https://mlsanity.com/paper/1601.04902/) (2016)
- [Labeled pupils in the wild: A dataset for studying pupil detection in unconstrained environments](https://mlsanity.com/paper/1511.05768/) (2015)
- [ElSe: Ellipse Selection for Robust Pupil Detection in Real-World Environments](https://mlsanity.com/paper/1511.06575/) (2015)
- [PupilNet v2.0: Convolutional Neural Networks for CPU based real time Robust Pupil Detection](https://mlsanity.com/paper/1711.00112/) (2017)
- [Fully Convolutional Neural Networks for Raw Eye Tracking Data Segmentation, Generation, and Reconstruction](https://mlsanity.com/paper/2002.10905/) (2020)
- [Rotated Ring, Radial and Depth Wise Separable Radial Convolutions](https://mlsanity.com/paper/2010.00873/) (2020)
- [Eye movement velocity and gaze data generator for evaluation, robustness testing and assess of eye tracking software and visualization tools](https://mlsanity.com/paper/1808.09296/) (2018)
- [Learning to Validate the Quality of Detected Landmarks](https://mlsanity.com/paper/1901.10143/) (2019)
- [Weight and Gradient Centralization in Deep Neural Networks](https://mlsanity.com/paper/2010.00866/) (2020)
- [Fast camera focus estimation for gaze-based focus control](https://mlsanity.com/paper/1711.03306/) (2017)
- [Eye movement simulation and detector creation to reduce laborious parameter adjustments](https://mlsanity.com/paper/1804.00970/) (2018)
- [Training Decision Trees as Replacement for Convolution Layers](https://mlsanity.com/paper/1905.10073/) (2019)
- [The Gaze and Mouse Signal as additional Source for User Fingerprints in Browser Applications](https://mlsanity.com/paper/2101.03793/) (2021)
- [Multi Layer Neural Networks as Replacement for Pooling Operations](https://mlsanity.com/paper/2006.06969/) (2020)
- [Explainable Online Validation of Machine Learning Models for Practical Applications](https://mlsanity.com/paper/2010.00821/) (2020)
- [A Multimodal Eye Movement Dataset and a Multimodal Eye Movement Segmentation Analysis](https://mlsanity.com/paper/2101.04318/) (2021)

## Built on by (most cited first, 7 of 7)

- [Maximum and Leaky Maximum Propagation](https://mlsanity.com/paper/2105.10277/) (2021)
- [Tensor Normalization and Full Distribution Training](https://mlsanity.com/paper/2109.02345/) (2021)
- [GroupGazer: A Tool to Compute the Gaze per Participant in Groups with integrated Calibration to Map the Gaze Online to a Screen or Beamer Projection](https://mlsanity.com/paper/2201.07692/) (2022)
- [Towards Unsupervised Eye-Region Segmentation for Eye Tracking](https://mlsanity.com/paper/2410.06131/) (2024)
- [Using Deep Learning to Increase Eye-Tracking Robustness, Accuracy, and Precision in Virtual Reality](https://mlsanity.com/paper/2403.19768/) (2024)
- [Technical Report: Combining knowledge from Transfer Learning during training and Wide Resnets](https://mlsanity.com/paper/2206.09697/) (2022)
- [GazeTrack: High-Precision Eye Tracking Based on Regularization and Spatial Computing](https://mlsanity.com/paper/2511.22607/) (2025)

## 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