GazeFollower: An open-source system for deep learning-based gaze tracking with web cameras
Gancheng Zhu, Xiaoting Duan, Zehao Huang, Rong Wang, Shuai Zhang, Zhiguo Wang · Proceedings of the ACM on Computer Graphics and Interactive Techniques · 2025
Gaze-tracking has a wide range of applications across scientific and industrial fields, and recent computer vision and deep-learning advances have made gaze-tracking with standard webcams possible. However, current solutions only offer suboptimal performance and lack flexibility. This paper introduces GazeFollower, an accessible system for webcam gaze-tracking in Python. GazeFollower stands out for its customizability, allowing researchers to quickly develop and adapt algorithms to meet their needs. At its core, GazeFollower estimates gaze with a model trained on 32 million face images, ensuring robust gaze tracking. A benchmark test on a sizeable sample (N=31) shows that the tracking performance of GazeFollower is on par with or better than budget commercial eye trackers. With calibration, GazeFollower has an accuracy of 1.11 cm and a precision of 0.11 cm, and personalized model fine-tuning further enhances these metrics to 0.92 cm and 0.08 cm. These results suggest that GazeFollower holds potential for real-world applications.