WebEyeTrack
Calibrated Real-Time Eye Tracking
RUN
This tool integrates RedForestAI/WebEyeTrack into the EZ-MMLA
Toolkit as a browser-based webcam eye-tracking workflow.
WebEyeTrack combines MediaPipe face landmarks with a TensorFlow.js gaze model to estimate a point of gaze on the screen. This Toolkit version adds a guided two-round calibration routine: one round on a white screen and one round on a black screen, with 25 target points in each round.
The run page follows the same Toolkit workflow as the other webcam tools: camera capture, tagging, recording controls, downloadable CSV predictions, and an overview panel with session statistics and a gaze scatter plot.
| Output | Description |
|---|---|
x, y |
Estimated gaze position in screen pixels. |
norm_x, norm_y |
WebEyeTrack normalized point of gaze in the -0.5 to 0.5 range. |
gaze_state |
Whether the model considers the eyes open or closed. |
face_visible |
Whether face landmarks were detected for that sample. |
total_ms |
Total per-frame processing time reported by WebEyeTrack. |