Automated gaze direction scoring from videos collected online through conventional webcam.

As online research has become more prevalent, researchers have been investigating the possibility of replicating techniques that go beyond measuring only simple behaviour. One such method could leverage the webcam of the participants’ device to collect information about eye gaze direction. Several packages have been developed for collecting such data, but they all lead to high attrition and require extensive and potentially frustrating calibration procedures, which hinders all research, in particular data collection including children and participants with neuro-developmental difficulties.To overcome this issue, we developed GazeScorer, a package that uses basic image processing techniques and a simple one-point calibration to score horizontal gaze orientation. We based our work on experience gained from infant research, in which a researcher manually scores horizontal gaze orientation from individual video frames. Using videos collected in two browser-based remote studies, one including adults, and one including children. We achieved low participant attrition, single-frame point calibration, and demonstrated a good level of inter-rater reliability between GazeScorer and a manual scorer. Our package provides a potential resource for researchers working with populations who could not perform a long or involved calibration, in studies in which information about horizontal gaze orientation is sufficient.