  Automated co-located human-human interaction analysis has been addressed by
the use of nonverbal communication as measurable evidence of social and
psychological phenomena. We survey the computing studies (since 2010) detecting
phenomena related to social traits (e.g., leadership, dominance, personality
traits), social roles/relations, and interaction dynamics (e.g., group
cohesion, engagement, rapport). Our target is to identify the nonverbal cues
and computational methodologies resulting in effective performance. This survey
differs from its counterparts by involving the widest spectrum of social
phenomena and interaction settings (free-standing conversations, meetings,
dyads, and crowds). We also present a comprehensive summary of the related
datasets and outline future research directions which are regarding the
implementation of artificial intelligence, dataset curation, and
privacy-preserving interaction analysis. Some major observations are: the most
often used nonverbal cue, computational method, interaction environment, and
sensing approach are speaking activity, support vector machines, and meetings
composed of 3-4 persons equipped with microphones and cameras, respectively;
multimodal features are prominently performing better; deep learning
architectures showed improved performance in overall, but there exist many
phenomena whose detection has never been implemented through deep models. We
also identified several limitations such as the lack of scalable benchmarks,
annotation reliability tests, cross-dataset experiments, and explainability
analysis.
