Face expression understanding by geometrical characterization of deep human face representation

Abstract

Face expressions understanding is a key to have a better understanding of the human nature. In this contribution we propose an end-to-end pipeline that takes color images as inputs and produces a semantic graph that encodes numerically what are facial emotions. This approach leverages low-level geometric details as face representation which are numerical representations of facial muscle activation patterns to build this emotional understanding. It shows that our method recovers social expectations of what characterize facial emotions.