Automatic Detection of Facial Actions from 3D Data

Abstract

We address the person-independent recognition problem of facial expressions using static 3D face data. The novel approach to the facial expression recognition uses non-rigid registration of surface curvature features. 3D face data is cast onto 2D feature images, which are then subjected to elastic deformations in their parametric space. Each Action Unit (AU) detector is trained over its respective influence domain on the face. The registration task is incorporated in the multiresolution elastic deformation scheme, which yields adequate registration accuracy for mild pose variations. The algorithm is fully automatic and is free of the burden of first localizing anatomical facial points. The algorithm was tested on 22 facial action units of Facial Action Coding System. Promising results obtained indicate that we have an operative device for facial action unit detection, and an intermediate step to infer emotional or mental states. Moreover, experiments conducted with low intensity AU12 - Lip Corner Puller points to the potential of 3D data and the proposed method in subtle expression detection.