Qick bootstrapping of a Personalized gaze model from real-use interactions

Michael Xuelin Huang, Jiajia Li, Grace Ngai, Hong Va Leong

Research output: Journal article publicationJournal articleAcademic researchpeer-review

3 Citations (Scopus)


Understanding human visual attention is essential for understanding human cognition, which in turn benefits human-computer interaction. Recent work has demonstrated a Personalized, Auto-Calibrating Eye-tracking (PACE) system, which makes it possible to achieve accurate gaze estimation using only an off-the-shelf webcam by identifying and collecting data implicitly from user interaction events. However, this method is constrained by the need for large amounts of well-annotated data. We thus present fast-PACE, an adaptation to PACE that exploits knowledge from existing data from different users to accelerate the learning speed of the personalized model. The result is an adaptive, data-driven approach that continuously "learns" its user and recalibrates, adapts, and improves with additional usage by a user. Experimental evaluations of fast-PACE demonstrate its competitive accuracy in iris localization, validity of alignment identification between gaze and interactions, and effectiveness of gaze transfer. In general, fast-PACE achieves an initial visual error of 3.98 degrees and then steadily improves to 2.52 degrees given incremental interaction-informed data. Our performance is comparable to state-of-the-art, but without the need for explicit training or calibration. Our technique addresses the data quality and quantity problems. It therefore has the potential to enable comprehensive gaze-aware applications in the wild.
Original languageEnglish
Article numbera43
JournalACM Transactions on Intelligent Systems and Technology
Issue number4
Publication statusPublished - 1 Jan 2018


  • Data validation
  • Gaze estimation
  • Gaze transfer learning
  • Gaze-interaction alignment
  • Implicit modeling

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Artificial Intelligence


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