The analysis of dynamic scenes has been a challenging domain in eye tracking research. This study presents a framework, named MAGiC, for analyzing gaze contact and gaze aversion in face-to-face communication. MAGiC provides an environment that is able to detect and track the conversation partner’s face automatically, overlay gaze data on top of the face video, and incorporate speech by means of speech-act annotation. Specifically, MAGiC integrates eye tracking data for gaze, audio data for speech segmentation, and video data for face tracking. MAGiC is an open source framework and its usage is demonstrated via publicly available video content and wiki pages. We explored the capabilities of MAGiC through a pilot study and showed that it facilitates the analysis of dynamic gaze data by reducing the annotation effort and the time spent for manual analysis of video data
