WEBEYETRACK: Scalable Eye-Tracking for the Browser via On-Device Few-Shot Personalization
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arXiv
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909755093549056 |
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| author | Davalos, Eduardo Zhang, Yike Srivastava, Namrata Thatigotla, Yashvitha Salas, Jorge A. McFadden, Sara Cho, Sun-Joo Goodwin, Amanda TS, Ashwin Biswas, Gautam |
| author_facet | Davalos, Eduardo Zhang, Yike Srivastava, Namrata Thatigotla, Yashvitha Salas, Jorge A. McFadden, Sara Cho, Sun-Joo Goodwin, Amanda TS, Ashwin Biswas, Gautam |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_19544 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | WEBEYETRACK: Scalable Eye-Tracking for the Browser via On-Device Few-Shot Personalization Davalos, Eduardo Zhang, Yike Srivastava, Namrata Thatigotla, Yashvitha Salas, Jorge A. McFadden, Sara Cho, Sun-Joo Goodwin, Amanda TS, Ashwin Biswas, Gautam Computer Vision and Pattern Recognition Artificial Intelligence 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. |
| title | WEBEYETRACK: Scalable Eye-Tracking for the Browser via On-Device Few-Shot Personalization |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2508.19544 |