WebEyeTrack: Scalable Eye-Tracking for the Browser via On-Device Few-Shot Personalization

With advancements in AI, appearance-based gaze estimationmethods have improved benchmark performance, but practical deploy-ment still lags behind commercial eye-trackers. Factors like model size,inference time, and privacy often go unaddressed. Meanwhile, webcam-based eye-tracking methods remain sensitive to head movement. To tacklethese issues, we introduce WebEyeTrack, a browser-native frameworkthat integrates a novel lightweight gaze model, metric head pose estima-tion, and on-device few-shot learning with as few as nine per-user calibra-tion samples (k ≤ 9). WebEyeTrack adapts to new users, achieving com-petitive performance with an error margin of 2.32 cm on GazeCaptureand real-time inference latency of 2.4 milliseconds on an iPhone 14. Ouropen-source code is available at github.com/RedForestAI/WebEyeTrack.