Dynamic face imaging: a novel analysis framework for 4D social face perception and expression

Measuring facial expressions is a notoriously difficult and time-consuming process, often involving manual labeling of low-dimensional descriptors such as Action Units (AUs). Computer vision algorithms provide automated alternatives for measuring and annotating face shape and expression from 2D images, but often ignore the complexities of dynamic 3D facial expressions. Moreover, open-source implementations are often difficult to use, preventing widespread adoption by the wider scientific community beyond computer vision. To address these issues, we develop dynamic face imaging, a novel analysis framework to study social face perception and expression. We use state-of-the-art 3D face reconstruction models to quantify face movement as temporal shape deviations in a common 3D mesh topology, which disentangles global (head) movement and local (facial) movement. Using a set of validation analyses, we test different reconstruction algorithms and quantify how well they reconstruct facial “action units” and track key facial landmarks in 3D, demonstrating promising performance and highlight areas for improvement. We provide an open-source software package that implements functionality for easy reconstruction, preprocessing, and analysis of these dynamic facial expression data.