Real-Time Webcam-Based Eye Tracking for Gaze Estimation: Applications and Innovations

Webcam-based eye-tracking systems offer a costeffective and accessible solution for capturing eye movements and gaze direction, facilitating applications in human-computer interaction (HCI), virtual reality (VR), and market research. This paper presents a novel webcam-based eye-tracking system, addressing accessibility, affordability, and usability by leveraging ubiquitous webcam technology, eliminating the need for specialized hardware. The methodology improves gaze estimation accuracy and stability by addressing inherent noise and variability by an average of 79 percent. A Multioutput Regressor with Support Vector Regression (SVR) as the base model, refined through linear regression, aligns predicted gaze points with actual screen coordinates. The Kalman filter stabilizes tracking by recursively predicting and updating state estimates. A responsive web application was developed using Flask, HTML, CSS, and JavaScript. Real time pupil detection was facilitated by MediaPipe, with data transmitted via Web-Sockets using the Socket.IO library. Frame-by-frame data collection, processed through stacking models and Kalman filtering, enhances accuracy and precision. Applications include early detection of neurological diseases like Parkinson’s, Alzheimer’s, and autism spectrum disorder (ASD), and assisting individuals with disabilities in controlling devices via eye movements. In education, eye-tracking enhances student engagement and personalized learning. Businesses can optimize product placement and advertisement effectiveness. In gaming and VR, integrating eye-tracking enhances natural interactions and immersive experiences. Future developments focus on enhancing algorithmic performance, integrating with AI, AR, and VR, and providing real-time feedback. Ethical guidelines and privacy safeguards are crucial for data security, democratizing eye-tracking technology for diverse applications.