Towards End to End head-free gaze classification
Rakshit Kothari, Zhizhuo Yang, Chris Kanan, Jeff B. Pelz, Reynold Bailey, Gabriel J. Diaz · Journal of Vision · 2019
The study of gaze behavior benefits from the classification of the time series into distinct movement types, or events, such as saccade, pursuit, and fixation. Because the manual identification of events is time consuming and subjective, there is a need for automated classifiers. Although there are several solutions for the classification of the eye-in-head signal, there are no established solutions for classification of the coordinated movements of the eyes and head that will occur in less constrained contexts, for example, when wearing a virtual or augmented reality display. Our approach involves training various temporal classifiers on our new Gaze-in-Wild dataset, recorded from over 20 unrestrained participants and hand-coded by 5 practiced labellers. Subjects were instrumented with a 6-axis 100 Hz inertial measurement unit (mean drift: 0.03 deg/sec), a 30 Hz ZED stereo camera, and a 120Hz Pupil labs eye tracker (mean calibration AngError kappa 0.70, event F1> 0.85).