Implicit User Calibration for Gaze-tracking Systems Using Saliency Maps Filtered by Eye Movements

In recent studies on gaze tracking systems using 3D model-based methods, the optical axis of the eye was estimated without user calibration. The remaining challenge in achieving implicit user calibration is estimating the difference between the optical and visual axes of the eye (angle κ). In this study, we propose two methods that improve the implicit user calibration method using saliency maps, focusing on eye movement to reduce calculation costs while maintaining accuracy.