(КПІ ім. Ігоря Сікорського, 2025) Oliinyk, V.; Korol, S.
This paper presents a gaze tracking method based on a hybrid gaze direction prediction model, designed for real real-time operation in web applications under limited computational resources and without specialized hardware. The proposed approach combines geometric normalization of facial landmarks with a lightweight CNN CNN-Transformer network to estimate gaze direction and project it onto 2D screen coordinates. Designed for scalable and privacy privacy-preserving use in web applications, it addresses the limitations of appearanceappearance-only and geometry geometry-only methods. The system uses MediaPipe FaceMesh for 3D landmark detection, followed by normalization, hybrid gaze estimation, and a 9 9-point calibration procedure using regression regression-based mapping. A comprehensive experimental setup was developed to evaluate i ts effectiveness. Results demonstrate that our approach achieves high angular accuracy and lower jitter during a user active head movement, with real-time inference running entirely in-browser using ONNX Web Runtime. The proposed method is suitable for use in adaptive web interfaces, assistive technologies, educational tools, and behavioral research applications. It offers an accessible pathway for integrating gaze-based interaction into widespread browser platforms without the need for dedicated hardware.