CrowdEyes: crowdsourcing for robust real-world mobile eye tracking

Mohammad Othman, Telmo Amaral, Róisín McNaney, Jan D. Smeddinck, John Vines, Patrick Olivier

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

4 Citations (Scopus)


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
Title of host publicationMobileHCI '17 - Proceedings of the 19th International Conference on Human-Computer Interaction with Mobile Devices and Services
EditorsRoderick Murray-Smith, Yvonne Rogers
Place of PublicationNew York NY USA
PublisherAssociation for Computing Machinery (ACM)
Number of pages13
ISBN (Electronic)9781450350754
Publication statusPublished - 2017
Externally publishedYes
EventInternational Conference on Human-Computer Interaction with Mobile Devices and Services 2017 - Vienna, Austria
Duration: 4 Sep 20177 Sep 2017
Conference number: 19th (Proceedings)


ConferenceInternational Conference on Human-Computer Interaction with Mobile Devices and Services 2017
Abbreviated titleMobileHCI 2017
Internet address


  • Crowd quality control
  • Crowdsourcing
  • Eye tracking
  • Mobile computing
  • Pupil detection
  • Wearable computing

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