Building a self-learning eye gaze model from user interaction data

Michael Xuelin Huang, Tiffany C.K. Kwok, Grace Ngai, Hong Va Leong, Stephen C.F. Chan

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

19 Citations (Scopus)


Most eye gaze estimation systems rely on explicit calibration, which is inconvenient to the user, limits the amount of possible training data and consequently the performance. Since there is likely a strong correlation between gaze and interaction cues, such as cursor and caret locations, a supervised learning algorithm can learn the complex mapping between gaze features and the gaze point by training on incremental data collected implicitly from normal computer interactions. We develop a set of robust geometric gaze features and a corresponding data validation mechanism that identifies good training data from noisy interaction-informed data collected in real-use scenarios. Based on a study of gaze movement patterns, we apply behavior-informed validation to extract gaze features that correspond with the interaction cue, and data-driven validation provides another level of crosschecking using previous good data. Experimental evaluation shows that the proposed method achieves an average error of 4.06°, and demonstrates the effectiveness of the proposed gaze estimation method and corresponding validation mechanism.
Original languageEnglish
Title of host publicationMM 2014 - Proceedings of the 2014 ACM Conference on Multimedia
PublisherAssociation for Computing Machinery, Inc
Number of pages4
ISBN (Electronic)9781450330633
Publication statusPublished - 1 Jan 2014
Event2014 ACM Conference on Multimedia, MM 2014 - Orlando, United States
Duration: 3 Nov 20147 Nov 2014


Conference2014 ACM Conference on Multimedia, MM 2014
Country/TerritoryUnited States


  • Data validation
  • Gaze estimation
  • Implicit modeling
  • Supervised learning

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Computer Vision and Pattern Recognition
  • Media Technology
  • Software


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