Talk · IEEE International Conference on AI Engineering and Innovations · March 26, 2026
Real Time Screen Based Gaze Estimation Using MediaPipe and Machine Learning Models
Sahil Kumar Gupta, Aaditya Yadav, Adrija Adhikary, Raushan Kumar Gupta, C. Jyotsna, J. Amudha
Abstract
Gaze estimation is a fundamental component in human–computer interaction, assistive technologies, and behavioral analysis. Conventional gaze-tracking systems rely on infrared sensors or head-mounted hardware, which are expensive and difficult to scale for real-world deployment. Moreover, software-based approaches often require complex calibration, reducing usability. The proposed work presents an efficient machine learning–based gaze-tracking system that operates using standard webcams, thereby eliminating the need for additional equipment. The proposed system employs MediaPipe for facial landmark localization and a Support Vector Regressor (SVR) to estimate gaze direction from extracted facial features. Frame segmentation analysis incorporating pitch, yaw, and roll parameters further enhances prediction accuracy. Experimental evaluations demonstrate high real-time precision, supporting applications in behavioral research, heatmap generation, and user engagement assessment. The proposed framework offers a cost-effective, hardware-independent, and easily deployable alternative to traditional gaze-tracking systems.
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