mlsanityData to 14 Oct 2026

The Gaze and Mouse Signal as additional Source for User Fingerprints in Browser Applications

arXiv 2101.03793 ↗First posted 11 Jan 2021cs.CVcited by 10

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Abstract (arXiv)

In this work, we inspect different data sources for browser fingerprints. We show which disadvantages and limitations browser statistics have and how this can be avoided with other data sources. Since human visual behavior is a rich source of information and also contains person specific information, it is a valuable source for browser fingerprints. However, human gaze acquisition in the browser also has disadvantages, such as inaccuracies via webcam and the restriction that the user must first allow access to the camera. However, it is also known that the mouse movements and the human gaze correlate and therefore, the mouse movements can be used instead of the gaze signal. In our evaluation, we show the influence of all possible combinations of the three information sources for user recognition and describe our simple approach in detail. Link: https://es-cloud.cs.uni-tuebingen.de/d/8e2ab8c3fdd444e1a135/?p=%2FThe%20Gaze%20and%20Mouse%20Signal%20as%20additional%20Source%20...&mode=list

Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Builds on 14 of its 84 references that are in the map. First posted on arXiv 11 Jan 2021.

Builds on

14 of 84 references.

References · 14

  1. 2016PupilNet: Convolutional Neural Networks for Robust Pupil Detectioncited by 20
  2. 2015ElSe: Ellipse Selection for Robust Pupil Detection in Real-World Environmentscited by 16
  3. 2020Fully Convolutional Neural Networks for Raw Eye Tracking Data Segmentation, Generation, and Reconstructioncited by 12
  4. 2020Rotated Ring, Radial and Depth Wise Separable Radial Convolutionscited by 12
  5. 2018Eye movement velocity and gaze data generator for evaluation, robustness testing and assess of eye tracking software and visualization toolscited by 11
  6. 2019Learning to Validate the Quality of Detected Landmarkscited by 11
  7. 2020Weight and Gradient Centralization in Deep Neural Networkscited by 11
  8. 2017Fast camera focus estimation for gaze-based focus controlcited by 10
  9. 2018Eye movement simulation and detector creation to reduce laborious parameter adjustmentscited by 10
  10. 2019Training Decision Trees as Replacement for Convolution Layerscited 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
2 more
  1. 2019Clicktok: Click Fraud Detection using Traffic Analysiscited by 2
  2. 2018Detection of Fraudulent Sellers in Online Marketplaces using Support Vector Machine Approachcited by 1

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Cited by year

Citing papers per year: 2021: 4; 2022: 3; 2023: 2; 2024: 1.

10 papers.

  1. 2021TEyeD: Over 20 million real-world eye images with Pupil, Eyelid, and Iris 2D and 3D Segmentations, 2D and 3D Landmarks, 3D Eyeball, Gaze Vector, and Eye Movement Typescited by 32
  2. 20211000 Pupil Segmentations in a Second using Haar Like Features and Statistical Learningcited by 7
  3. 2021Maximum and Leaky Maximum Propagationcited by 4
  4. 2021Tensor Normalization and Full Distribution Trainingcited by 3
  5. 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
  6. 2022Gaze-based Object Detection in the Wildcited by 1
  7. 2023Area of interest adaption using feature importancecited by 1
  8. 2022Technical Report: Combining knowledge from Transfer Learning during training and Wide Resnetscited by 0
  9. 2023Teaching Unknown Objects by Leveraging Human Gaze and Augmented Reality in Human-Robot Interactioncited by 0
  10. 2024A Trainable Feature Extractor Module for Deep Neural Networks and Scanpath Classificationcited by 0