Estimation of facial action intensities on 2D and 3D data

The paradigm of Facial Action Coding System (FACS) offers a comprehensive solution for facial expression measurements. FACS defines atomic expression components called Action Units (AUs) and describes their strength on a five-point scale. Despite considerable progress in AU detection, the AU intensity estimation has not been much investigated. We propose SVM-based regression on AU feature space, and investigate person-independent estimation of 25 AUs that appear singly or in various combinations. Our method is novel in that we use regression for estimating intensities and comparatively evaluate the performances of 2D and 3D modalities. The proposed technique shows improvements over the state-of-the-art person-independent estimation, and that especially the 3D modality offers significant advantages for intensity coding. We have also found that fusion of 2D and 3D can boost the estimation performance, especially when modalities compensate for each other's shortcomings.