Current eye-trackers generally rely on previous-generation computer-vision algorithms and the best ones are also expensive and closed-source. A recent publication from Google Research has shown that it is possible to obtain very good performance using a simple convolutional neural network (CNN) running off a simple mobile phone camera. The details of the CNN have been made available, but the actual implementation is not available. The goal of the project is to implement this algorithm in an open-source package and then explore various extensions including a) incorporating head-position estimation for eye-in-head measurements; and b) extending the algorithm to higher sampling-rates and incorporating filtered estimates of eye-position that take the time-series of previously estimated eye-positions into account.
Mentor: Suresh Krishna @suresh.krishna
