WebGazer.js is an eye tracking library that uses common webcams to infer the eye-gaze locations of web visitors on a page in real time. The eye tracking model it contains self-calibrates by watching web visitors interact with the web page and trains a mapping between the features of the eye and positions on the screen. WebGazer.js is written entirely in JavaScript and with only a few lines of code can be integrated in any website that wishes to better understand their visitors and transform their user experience. WebGazer.js runs entirely in the client browser, so no video data needs to be sent to a server, and it requires the user's consent to access their webcam.
See how easy it is to integrate WebGazer.js on any webpage. With just a few clicks you will get real-time predictions. Follow the popup instructions to click through 9 calibration points on the screen whilst looking at the cursor.
Move the orange ball with your eyes and create collisions with the blue balls. Train WebGazer.js by clicking in various locations within the screen, while looking at your cursor.
Webgazer is developed based on the research that is done by Brown University. The work of the calibration example file was developed in the context of a course project with the aim to improve the feedback of WebGazer, proposed by Dr. Gerald Weber and his team Dr. Clemens Zeidler and Kai-Cheung Leung.
This research is supported by NSF grants IIS-1464061, IIS-1552663, a Seed Award from the Center for Vision Research at Brown University, and the Brown University Salomon Award.