Computational Social Dynamics: Analyzing the Face-level Interactions in a Group
Nicholas Watkins, Ifeoma Nwogu

TL;DR
This paper presents a novel deep neural network architecture using LSTMs to measure facial interactional synchrony in group conversations, demonstrating high accuracy on synthetic and real datasets.
Contribution
It introduces a new deep learning model that captures nonlinear dependencies in facial cues to quantify interactional synchrony in social groups.
Findings
Achieved 0.5% error in synthetic data covariance estimation.
Estimated group synchrony with 2.96% mean error on real data.
Outperformed baseline permutation tests significantly.
Abstract
Interactional synchrony refers to how the speech or behavior of two or more people involved in a conversation become more finely synchronized with each other, and they can appear to behave almost in direct response to one another. Studies have shown that interactional synchrony is a hallmark of relationships, and is produced as a result of rapport. %Research has also shown that up to two-thirds of human communication occurs via nonverbal channels such as gestures (or body movements), facial expressions, \etc. In this work, we use computer vision based methods to extract nonverbal cues, specifically from the face, and develop a model to measure interactional synchrony based on those cues. This paper illustrates a novel method of constructing a dynamic deep neural architecture, specifically made up of intermediary long short-term memory networks (LSTMs), useful for learning and…
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Taxonomy
TopicsFace Recognition and Perception · Action Observation and Synchronization · Face recognition and analysis
