Computer Laboratory


Teaser figure
EyeTab enables gaze-based interaction with unmodified tablet computers in near realtime using a model-based approach for binocular gaze estimation. We fit an ellipse model to irises in the image, and thus estimate the position and orientation of the eyes in 3-D space.


Despite the widespread use of mobile phones and tablets, hand-held portable devices were only recently identified as a promising platform for gaze-aware applications. Estimating gaze on portable devices is challenging given the limited computational resources, the low quality of the integrated front-facing RGB camera, and the small screen to which gaze is mapped. In this paper we present EyeTab, a model-based approach for binocular gaze estimation that runs entirely on an unmodified tablet. EyeTab builds on set of established image processing and computer vision algorithms and adapts them for robust and near-realtime gaze estimation. A technical prototype evaluation with eight participants in a normal indoors office setting shows that EyeTab achieves an average gaze estimation accuracy of 6.88° of visual angle at 12 frames per second.



  author  = {Erroll Wood and Andreas Bulling},
  title   = {EyeTab: Model-based gaze estimation on unmodified tablet computers},
  booktitle = {Proceedings of ETRA},
  month     = mar,
  year      = {2014},
  location  = {Safety Harbour, Florida},
  url       = {},