Graphics enhanced version of this site
Neural Network Gaze Tracking
This project is no longer active.
Head: Dean Pomerleau
Contact: Shumeet Baluja (baluja@cs.cmu.edu)
Mailing address:
Carnegie Mellon University
Robotics Institute
5000 Forbes Avenue
Pittsburgh, PA 15213
Associated center: VASC
The system described here attempts to perform non-intrusive gaze tracking, in which the user is neither required to wear any special equipment, nor required to keep his/her head still.
We have created a non-intrusive gaze tracking system which is based upon a simple artificial neural network. Unlike other gaze-tracking systems which use traditional methods, such as a edge detection and circle fitting, this system develops its own features for successfully completing the task. The system's average on-line accuracy is 1.7 degrees. It has successfully been used in human-computer interaction studies and as an input device.
we hope to increase the system's accuracy without the addition of any intrusive hardware. Although we do not have as much invariance to head position as is desired, head position is not unnaturally restrained, and the user does not wear any extraneous equipment. This already makes the connectionist gaze tracker much less intrusive than many existing systems. We would like to test the viability of entirely replacing the mouse with the connectionist gaze tracker. Other potential uses for the system include aiding disabled people in interacting with their environment, and as a tool for data collection in psychological and human-computer interaction experiments.
Note: This list may not be comprehensive. It contains only those publications in the RI publications database. Entries are listed in reverse chronological order.