Neural networks for optical vector and eye ball parameter estimation

Wolfgang Fuhl, Hong Gao, Enkelejda Kasneci

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

14 Scopus citations

Abstract

In this work we evaluate neural networks, support vector machines and decision trees for the regression of the center of the eyeball and the optical vector based on the pupil ellipse. In the evaluation we analyze single ellipses as well as window-based approaches as input. Comparisons are made regarding accuracy and runtime. The evaluation gives an overview of the general expected accuracy with different models and amounts of input ellipses. A simulator was implemented for the generation of the training and evaluation data. For a visual evaluation and to push the state of the art in optical vector estimation, the best model was applied to real data. This real data came from public data sets in which the ellipse is already annotated by an algorithm. The optical vectors on real data and the generator are made publicly available. Link to the generator and models.

Original languageEnglish
Title of host publicationProceedings ETRA 2020 Short Papers - ACM Symposium on Eye Tracking Research and Applications, ETRA 2020
EditorsStephen N. Spencer
PublisherAssociation for Computing Machinery
ISBN (Electronic)9781450371346
DOIs
StatePublished - 6 Feb 2020
Externally publishedYes
Event2020 ACM Symposium on Eye Tracking Research and Applications, ETRA 2020 - Stuttgart, Germany
Duration: 2 Jun 20205 Jun 2020

Publication series

NameEye Tracking Research and Applications Symposium (ETRA)

Conference

Conference2020 ACM Symposium on Eye Tracking Research and Applications, ETRA 2020
Country/TerritoryGermany
CityStuttgart
Period2/06/205/06/20

Keywords

  • Data set
  • Eye tracking
  • Gaze vector estimation
  • Machine learning
  • Pupil ellipse generator
  • Runtime comparison

Fingerprint

Dive into the research topics of 'Neural networks for optical vector and eye ball parameter estimation'. Together they form a unique fingerprint.

Cite this