Building a Personalized, Auto-Calibrating Eyetracker from user interactions

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

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

53 Citations (Scopus)


We present PACE, a Personalized, Auto-Calibrating Eyetracking system that identifies and collects data unobtrusively from user interaction events on standard computing systems without the need for specialized equipment. PACE relies on eye/facial analysis of webcam data based on a set of robust geometric gaze features and a two-layer data validation mechanism to identify good training samples from daily interaction data. The design of the system is founded on an in-depth investigation of the relationship between gaze patterns and interaction cues, and takes into consideration user preferences and habits. The result is an adaptive, data-driven approach that continuously recalibrates, adapts and improves with additional use. Quantitative evaluation on 31 subjects across different interaction behaviors shows that training instances identified by the PACE data collection have higher gaze point-interaction cue consistency than those identified by conventional approaches. An in-situ study using real-life tasks on a diverse set of interactive applications demonstrates that the PACE gaze estimation achieves an average error of 2.56°, which is comparable to state-of-theart, but without the need for explicit training or calibration. This demonstrates the effectiveness of both the gaze estimation method and the corresponding data collection mechanism.
Original languageEnglish
Title of host publicationCHI 2016 - Proceedings, 34th Annual CHI Conference on Human Factors in Computing Systems
PublisherAssociation for Computing Machinery
Number of pages11
ISBN (Electronic)9781450333627
Publication statusPublished - 7 May 2016
Event34th Annual Conference on Human Factors in Computing Systems, CHI 2016 - San Jose Convention Center, San Jose, United States
Duration: 7 May 201612 May 2016


Conference34th Annual Conference on Human Factors in Computing Systems, CHI 2016
Country/TerritoryUnited States
CitySan Jose


  • Data validation
  • Gaze estimation
  • Gazeinteraction correspondence
  • Implicit modeling

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Computer Graphics and Computer-Aided Design
  • Software


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