Detecting 3D facial Action Units via registration

Abstract

Due to its potential for human-computer interfaces and human facial behavior research, automatic analysis of facial expressions has been an active area of study. In this paper a novel data-driven approach is proposed to detect Action Units (AUs) on 3D faces. With this approach, it is possible to design detectors that can perform detailed face registration without resorting to any face modeling, hence can compensate confounding effects like pose and physiognomy differences and can process facial features more effectively, however, without and drawbacks of model-driven analysis. This is the first example of detailed registration in data-driven expression analysis and surpasses state-of-the-art AU detection.