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The paper introduces FIDAC, an open-sourced library designed to extract and interpret interpersonal distance from video by leveraging facial detection results. By integrating data from multiple facial detection models and implementing advanced tracking methods, FIDAC addresses the challenges of depth distortion and model limitations in measuring interpersonal distance. Initial evaluations suggest that FIDAC can significantly enhance the accuracy of interpersonal distance analysis, paving the way for future research in proxemic analysis.
FIDAC reveals how interpersonal distance can be accurately quantified from video, transforming facial detection data into actionable insights.
The distance between persons reveals significant information about their perception of each other. However, such information is not easily extractable and interpretable from video input. We developed an open-sourced library, Facial Interpersonal Distance Analysis and Coding (FIDAC) that transforms facial detection results into actionable data about location and interpersonal distance. This tool merges data from multiple open-source facial detection models, strategically compensating for gaps in any individual model. In addition, we include methods for more accurate tracking, such as a pipeline for human coding of the selection of faces and a benchmarking tool to reduce depth distortion. For next steps, we plan on building upon FIDAC by evaluating its effectiveness at measuring interpersonal distance at various depths and orientations while further integrating features of proxemic analysis such as synchrony into its software.
BAIR
Stanford HAI