Training machine learning algorithms for automatic facial coding: The role of emotional facial expressions' prototypicality.

ABSTRACT: Automatic facial coding (AFC) is a promising new research tool to efficiently analyze emotional facial expressions. AFC is based on machine learning procedures to infer emotion categorization from facial movements (i.e., Action Units). State-of-the-art AFC accurately classifies intense and prototypical facial expressions, whereas it is less accurate for non-prototypical and less intense facial expressions. A potential reason might be that AFC is typically trained with standardized and prototypical facial expression inventories. Because AFC would be useful to analyze less prototypical research material as well, we set out to determine the role of prototypicality in the training material. We trained established machine learning algorithms either with standardized expressions from widely used

SUBMITTER: Budenbender B 

PROVIDER: S-EPMC9916590 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature