Magnifying subtle facial motions for 4D Expression Recognition

In this paper, we propose an effective approach for automatic 4D Facial Expression Recognition (FER). The flow of 3D facial scans is first modeled to capture spatial deformations based on the recently-developed Riemannian approach, namely Dense Scalar Fields (DSF), where registration and comparison of neighboring 3D face frames are jointly led. The deformations are then fed into a temporal filtering based magnification step to amplify the slight facial actions over time. The proposed method allows revealing subtle (hidden) deformations which enhances the performance in classification. We evaluate our approach on the BU-4DFE dataset, and the state-of-art accuracy up to 94.18% is achieved, which is superior to the top one so far reported, clearly demonstrating its effectiveness.