Automated Identification of Areas of Interest in Dynamic Head-Mounted Display Videos for Forensic Psychology Applications

This paper introduces a novel software solution to address the challenges of automated identification of Areas of Interest (AOIs) in dynamic, head-mounted display virtual reality (VR) environments, with a focus on applica-tions in forensic psychology. Traditional eye-tracking tools often require manual annotation of AOIs when analyzing moving objects, such as faces, in dynamic 360-degree VR scenarios—a process that is time-intensive. The pre-sented software utilizes RetinaFace to generate consistent AOIs by dynami-cally tracking facial coordinates across video frames, accounting for variabil-ity in head movements. Outputs are seamlessly formatted for analysis in iMotions, enabling robust synchronization and visualization of gaze data. Validation against manual annotation confirms the system’s high accuracy (92.2%). By automating a previously manual process, the software provides researchers with an efficient and scalable tool for analyzing complex visual attention data, significantly enhancing the feasibility of large-scale VR stud-ies in forensic and psychological research. An empirical example illustrates how the software can be used.