Checkpoints for the models used in "Investigating Bias and Fairness in Appearance-based Gaze Estimation" (Akgül, Şahin & Kalkan, 2026).

Full code, training scripts, annotations, and documentation are on GitHub: akgulburak/gaze-estimation-fairness

Checkpoints for 5 architectures (CrossGaze, GazeTR, L2CS-Net, MCGaze, PureGaze), each trained under 4 conditions:

Each mitigation condition was trained separately targeting the ethnicity attribute and the gender attribute, all on the Gaze360 dataset.

We repeated each run 5 times to account for training variance. The _1-_5 suffix in each filename indicates the repetition index - these are independent runs of the same configuration, not different models.

See the GitHub repo for training/inference code.

This project is licensed under CC BY-NC-SA 4.0 - see LICENSE for details.