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 aversionin face-to-face communication. MAGiC provides an environment that is able to detectand track the conversation partner’s face automatically, overlay gaze data on top of the facevideo, and incorporate speech by means of speech-act annotation. Specifically, MAGiC integrateseye tracking data for gaze, audio data for speech segmentation, and video data forface tracking. MAGiC is an open source framework and its usage is demonstrated via publiclyavailable video content and wiki pages. We explored the capabilities of MAGiCthrough a pilot study and showed that it facilitates the analysis of dynamic gaze data byreducing the annotation effort and the time spent for manual analysis of video data.
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