Left Eye Position: (0, 0)
Right Eye Position: (0, 0)
Left Eye Blink: false
Right Eye Blink: false
Gaze Direction: (0, 0)
Webcam Eye Tracking System is a tool that uses a webcam to track the user's eye movements. The tool uses the TensorFlow.js library to detect the user's face and eyes, and then tracks the movement of the eyes in real-time. The tool can be used for a variety of applications, including user interface design, human-computer interaction, and accessibility.
The Webcam Eye Tracking System is an advanced, web-based tool designed to track eye movements using a standard webcam. This system harnesses the power of TensorFlow.js, an open-source library for machine learning in JavaScript, and BlazeFace, a lightweight model for face and landmark detection. TensorFlow.js enables efficient, real-time processing of the video stream directly in the browser, ensuring user privacy as no video data leaves the client's machine.
Key features of this tool include dynamic adjustment of eye tracking parameters such as eye width ratio, eye height ratio, and darkness threshold, which can be tuned in real-time to accommodate different users and lighting conditions. The system is capable of detecting eye positions, blink states, and gaze direction, making it suitable for a wide range of applications including user interface design, human-computer interaction, and accessibility enhancements.
The application's frontend is built using HTML and CSS, with JavaScript modules orchestrating the eye tracking functionalities. The modular JavaScript architecture includes components for video streaming (videoStream.js), face and eye detection (faceModel.js and faceDetection.js), and drawing utilities (drawingUtils.js) for rendering tracking results on the canvas overlay. This modular approach not only facilitates ease of maintenance but also enhances the system's adaptability for future upgrades or integration with other technologies.
Our commitment to user privacy and data security is paramount. The system processes all data locally on the user's device, and no video or personal data is transmitted externally. This approach not only ensures compliance with privacy standards but also optimizes performance by reducing server load and latency.
This Webcam Eye Tracking System was developed with the assistance of advanced AI technologies, including OpenAI's GPT-4 and GitHub Copilot. GPT-4, a state-of-the-art language model by OpenAI, provided guidance and suggestions on coding best practices, algorithmic design, and problem-solving approaches. GitHub Copilot, an AI pair programmer powered by OpenAI’s Codex, assisted in writing and refining code, contributing to the efficiency and effectiveness of the development process.
The use of these AI tools has enhanced the development workflow, enabling rapid prototyping and implementation of complex functionalities. However, it's important to note that the final design decisions, code validations, and quality assurance were carried out by human developers to ensure the reliability and integrity of the tool. The integration of AI in the development process represents our commitment to leveraging cutting-edge technologies to deliver innovative and high-quality solutions.