3D face recognition performance under adversarial conditions

We address the question of 3D face recognition and expression understanding under adverse conditions like illumination, pose, and accessories. We therefore conduct a campaign to build a 3D face database including systematic variation of poses, different types of occlusions, and a rich set of expressions. The expressions consist of a judiciously selected subset of Action Units as well as the six basic emotions. The database is designed to enable various research paths from face recognition to facial landmarking and to expression estimation. Preliminary results are presented on the outcome of three different landmarking methods as well as one registration method. As expected, observed non-neutral and non-frontal faces demand new robust algorithms to achieve an acceptable performance