GeoGaze: A Real-time, Lightweight Gaze Estimation Framework via Geometric Landmark Analysis

Gaze estimation plays a vital role in human-computer interaction, driver monitoring, and psychological analysis. While state-of-the-art appearance-based methods achieve high accuracy using deep learning, they often demand substantial computational resources, including GPU acceleration and extensive training, limiting their use in resource-constrained or real-time scenarios. This paper introduces GeoGaze, a novel, lightweight, training-free framework that infers categorical gaze direction (“Left”, “Center”, “Right”) solely from geometric analysis of facial landmarks. Leveraging the high-precision 478-point face mesh and iris landmarks provided by MediaPipe, GeoGaze computes a simple normalized iris-to-eye-corner ratio and applies intuitive thresholds, eliminating the need for model training or GPU support. Evaluated on a simulated 1,500-image dataset (SGDD-1500), GeoGaze delivers competitive directional classification accuracy while achieving real-time performance (~66 FPS on CPU), outperforming typical deep learning baselines by more than eight-fold in speed. These results position GeoGaze as an efficient, interpretable alternative for edge devices and applications where precise angular gaze is unnecessary and directional intent suffices.