A FACS valid 3D dynamic action unit database with applications to 3D dynamic morphable facial modeling

TLDR

The paper introduces the first dynamic 3D FACS dataset and proposes a framework for building dynamic 3D morphable models that eliminates the need for facial markers and reduces optical flow drift. The dataset comprises 10 subjects performing 19–97 action units, totaling 519 AU sequences, each peak frame manually coded by certified FACS experts, and the authors develop a novel AAM‑based 3D facial registration and mesh correspondence scheme. The dataset provides ground truth for 3D FACS‑based AU recognition, and the proposed registration method yields more reliable mesh correspondence, as demonstrated by the first quantitative assessment.

Abstract

This paper presents the first dynamic 3D FACS data set for facial expression research, containing 10 subjects performing between 19 and 97 different AUs both individually and in combination. In total the corpus contains 519 AU sequences. The peak expression frame of each sequence has been manually FACS coded by certified FACS experts. This provides a ground truth for 3D FACS based AU recognition systems. In order to use this data, we describe the first framework for building dynamic 3D morphable models. This includes a novel Active Appearance Model (AAM) based 3D facial registration and mesh correspondence scheme. The approach overcomes limitations in existing methods that require facial markers or are prone to optical flow drift. We provide the first quantitative assessment of such 3D facial mesh registration techniques and show how our proposed method provides more reliable correspondence.