Novel Generative Model for Facial Expressions Based on Statistical Shape Analysis of Landmarks Trajectories

International audienceWe propose a novel geometric framework for analyzing spontaneous facial expressions, with the specific goal of comparing, matching, and averaging the shapes of landmarks trajectories. Here we represent facial expressions by the motion of the landmarks across the time. The trajectories are represented by curves. We use elastic shape analysis of these curves to develop a Riemannian framework for analyzing shapes of these trajectories. In terms of empirical evaluation, our results on two databases: UvA-NEMO and Cohn-Kanade CK+ are very promising. From a theoretical perspective, this framework allows formal statistical inferences, such as generation of facial expressions