NetFACS: Using network science to understand facial communication systems

Alexander Mielke*, Bridget Waller, Claire Perez, Julie Duboscq, Jerome Micheletta*

*Corresponding author for this work

Research output: Working paper


Understanding facial signals in humans and other species is crucial for understanding the evolution, complexity, and function of the face as a communication system. The Facial Action Coding System (FACS) enables researchers to measure facial movements accurately, but we currently lack tools to reliably analyse data and efficiently communicate results. No statistical approach in facial signal research makes full use of the information encoded in FACS datasets. Network analysis can provide a way forward: by treating individual Action Units (the smallest units of facial movements) as nodes in a network and their co-occurrence as connections, we can analyse and visualise differences in the use of combinations of Action Units in different conditions. Here, we present ‘NetFACS’, a statistical package that uses occurrence probabilities and resampling methods to answer questions about the use of Action Units, Action Unit combinations, and the facial communication system as a whole in humans and non-human animals. Using human emotion signals as an example, we illustrate some of the current functionalities of NetFACS. We show that very few Action Units are specific to certain stereotypical emotion signals; that Action Units are not used independently from each other; that graph-level properties of stereotypical emotion signals differ; and that clusters of Action Units allow us to reconstruct facial signals, even when blind to the underlying conditions. The flexibility and widespread use of network analysis allows us to move away from studying facial signals as stereotyped expressions, and towards a dynamic and differentiated approach to facial communication.
Original languageEnglish
Publication statusPublished - 13 Nov 2020


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