Enhancing Smartphone Eye Tracking with Cursor-Based Interactive Implicit Calibration

要旨

The limited accuracy of eye-tracking on smartphones restricts its use. Existing RGB-camera-based eye-tracking relies on extensive datasets, which could be enhanced by continuous fine-tuning using calibration data implicitly collected from the interaction. In this context, we propose COMETIC (Cursor Operation Mediated Eye-Tracking Implicit Calibration), which introduces a cursor-based interaction and utilizes the inherent correlation between cursor and eye movement. By filtering valid cursor coordinates as proxies for the ground truth of gaze and fine-tuning the eye-tracking model with corresponding images, COMETIC enhances accuracy during the interaction. Both filtering and fine-tuning use pre-trained models and could be facilitated using personalized, dynamically updated data. Results show COMETIC achieves an average eye-tracking er- ror of 278.3 px (1.60 cm, 2.29◦), representing a 27.2% improvement compared to that without fine-tuning. We found that filtering cursor points whose actual distance to gaze is 150.0 px (0.86 cm) yields the best eye-tracking results.

著者
Chang Liu
Tsinghua University, BeiJing, China
Xiangyang Wang
Tsinghua University, Beijing, China
Chun Yu
Tsinghua University, Beijing, China
Yingtian Shi
Georgia Institute of Technology, Atlanta, Georgia, United States
Chongyang Wang
Sichuan University, Chengdu, Sichuan, China
Ziqi Liu
Tsinghua University, Beijing, Beijing, China
Chen Liang
The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, Guangdong, China
Yuanchun Shi
Qinghai University, Xining, Qinghai, China
DOI

10.1145/3706598.3713936

論文URL

https://dl.acm.org/doi/10.1145/3706598.3713936

動画

会議: CHI 2025

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2025.acm.org/)

セッション: Mobile Input

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7 件の発表
2025-05-01 18:00:00
2025-05-01 19:30:00
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