Gliding and Saccadic Gaze Gesture Recognition in Real Time (Rozado, 2011)

David Rozado with the Department of Neural Computation at the Universidad Autonoma de Madrid have developed a neural network approach for detecting gaze gestures in real time. I met David at ITU Copenhagen last summer when he was visiting and discussed this research, I'm happy to see that it came out with such great results. This research was part of Davids Ph.D thesis which focused on Hierarchical Temporal Memory (HTM) neural network which is a bioinspired pattern recognition algorithm. Using a low cost webcam and the ITU Gaze Tracker he is able to recognize ten different gestures with 90% accuracy using raw data. When a fixation detection algorithm and dwell time triggers are employed it is possible to achieve 100% detection rates (at the expense of longer activation times).