CrowdEyes: Crowdsourcing for Robust Real-World Mobile Eye Tracking

Mohammad Othman, Telmo Amaral, Roisin McNaney, Jan Smeddinck, John Vines, Patrick Olivier

Research output: Contribution to conferencePaperpeer-review

8 Citations (Scopus)
22 Downloads (Pure)


Current eye tracking technologies have a number of drawbacks when it comes to practical use in real-world settings. Common challenges, such as high levels of daylight, eyewear (e.g. spectacles or contact lenses) and eye make-up, give rise to noise that undermines their utility as a standard component for mobile computing, design, and evaluation. To work around these challenges, we introduce CrowdEyes, a mobile eye tracking solution that utilizes crowdsourcing for increased tracking accuracy and robustness. We present a pupil detection task design for crowd workers together with a study that demonstrates the high-level accuracy of crowdsourced pupil detection in comparison to state-of-the-art pupil detection algorithms. We further demonstrate the utility of our crowdsourced analysis pipeline in a fixation tagging task. In this paper, we validate the accuracy and robustness of harnessing the crowd as both an alternative and complement to automated pupil detection algorithms, and explore the associated costs and quality of our crowdsourcing approach.
Original languageEnglish
Publication statusPublished - 4 Sept 2017
EventMobileHCI 2017 - Vienna, Austria
Duration: 4 Sept 2017 → …


ConferenceMobileHCI 2017
Period4/09/17 → …
Internet address


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