  In this paper, an effective pipeline to automatic 4D Facial Expression
Recognition (4D FER) is proposed. It combines two growing but disparate ideas
in Computer Vision -- computing the spatial facial deformations using tools
from Riemannian geometry and magnifying them using temporal filtering. The flow
of 3D faces is first analyzed to capture the spatial deformations based on the
recently-developed Riemannian approach, where registration and comparison of
neighboring 3D faces are led jointly. Then, the obtained temporal evolution of
these deformations are fed into a magnification method in order to amplify the
facial activities over the time. The latter, main contribution of this paper,
allows revealing subtle (hidden) deformations which enhance the emotion
classification performance. We evaluated our approach on BU-4DFE dataset, the
state-of-art 94.18% average performance and an improvement that exceeds 10% in
classification accuracy, after magnifying extracted geometric features
(deformations), are achieved.
