Iris Geometric Transformation Guided Deep Appearance-Based Gaze Estimation

he geometric alterations in the iris’s appearance are intricately linked to the gaze direction. However, current deep appearance-based gaze estimation methods mainly rely on latent feature sharing to leverage iris features for improving deep representation learning, often neglecting the explicit modeling of their geometric relationships. To address this issue, this paper revisits the physiological structure of the eyeball and introduces a set of geometric assumptions, such as “the normal vector of the iris center approximates the gaze direction”. Building on these assumptions, we propose an Iris Geometric Transformation Guided Gaze estimation (IGTG-Gaze) module, which establishes an explicit geometric parameter sharing mechanism to link gaze direction and sparse iris landmark coordinates directly. Extensive experimental results demonstrate that IGTG-Gaze seamlessly integrates into various deep neural networks, flexibly extends from sparse iris landmarks to dense eye mesh, and consistently achieves leading performance in both within- and cross-dataset evaluations, all while maintaining end-to-end optimization. These advantages highlight IGTG-Gaze as a practical and effective approach for enhancing deep gaze representation from appearance.