A Validation of Automatically-Generated Areas-of-Interest in Videos of a Face for Eye-Tracking Research

When mapping eye-movement behavior to the visual information presented to an observer, Areas of Interest (AOIs) are commonly employed. For static stimuli (screen without moving elements), this requires that one AOI set is constructed for each stimulus, a possibility in most eye-tracker manufacturers' software. For moving stimuli (screens with moving elements), however, it is often a time-consuming process, as AOIs have to be constructed for each video frame. A popular use-case for such moving AOIs is to study gaze behavior to moving faces. Although it is technically possible to construct AOIs automatically, the standard in this field is still manual AOI construction. This is likely due to the fact that automatic AOI-construction methods are (1) technically complex, or (2) not effective enough for empirical research. To aid researchers in this field, we present and validate a method that automatically achieves AOI construction for videos containing a face. The fully-automatic method uses an open-source toolbox for facial landmark detection, and a Voronoi-based AOI-construction method. We compared the position of AOIs obtained using our new method, and the eye-tracking measures derived from it, to a recently published semi-automatic method. The differences between the two methods were negligible. The presented method is therefore both effective (as effective as previous methods), and efficient; no researcher time is needed for AOI construction. The software is freely available from https://osf.io/zgmch/.