Algorithm for Discriminating Aggregate Gaze Points: Comparison with Salient Regions-Of-Interest

A novel method for distinguishing classes of viewers from their aggregated eye movements is described. The probabilistic framework accumulates uniformly sampled gaze as Gaussian point spread functions (heatmaps), and measures the distance of unclassified scanpaths to a previously classified set (or sets). A similarity measure is then computed over the scanpath durations. The approach is used to compare human observers’s gaze over video to regions of interest (ROIs) automatically predicted by a computational saliency model. Results show consistent discrimination between human and artificial ROIs, regardless of either of two differing instructions given to human observers (free or tasked viewing).