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

• URL: http://arxiv.org/abs/2508.19544v1
• Date: Wed, 27 Aug 2025 03:38:58 GMT
• Title: WEBEYETRACK: Scalable Eye-Tracking for the Browser via On-Device Few-Shot Personalization
• Authors: Eduardo Davalos, Yike Zhang, Namrata Srivastava, Yashvitha Thatigotla, Jorge A. Salas, Sara McFadden, Sun-Joo Cho, Amanda Goodwin, Ashwin TS, Gautam Biswas,
• Abstract summary: We bEyeTrack is a framework that integrates lightweight SOTA gaze estimation models directly in the browser.<n>It incorporates model-based head pose estimation and on-device few-shot learning with as few as nine calibration samples.<n>WebEyeTrack adapts to new users, achieving SOTA performance with an error margin of 2.32 cm on GazeCapture and real-time inference speeds of 2.4 milliseconds on an iPhone 14.
• Score: 3.884714448890651
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With advancements in AI, new gaze estimation methods are exceeding state-of-the-art (SOTA) benchmarks, but their real-world application reveals a gap with commercial eye-tracking solutions. Factors like model size, inference time, and privacy often go unaddressed. Meanwhile, webcam-based eye-tracking methods lack sufficient accuracy, in particular due to head movement. To tackle these issues, we introduce We bEyeTrack, a framework that integrates lightweight SOTA gaze estimation models directly in the browser. It incorporates model-based head pose estimation and on-device few-shot learning with as few as nine calibration samples (k < 9). WebEyeTrack adapts to new users, achieving SOTA performance with an error margin of 2.32 cm on GazeCapture and real-time inference speeds of 2.4 milliseconds on an iPhone 14. Our open-source code is available at https://github.com/RedForestAi/WebEyeTrack.

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