Reshaping 3D facial scans for facial appearance modeling and 3D facial expression analysis

3D face scans have been widely used for face modeling and face analysis. Due to the fact that face scans provide variable point clouds across frames, they may not capture complete facial data or miss point-to-point correspondences across various facial scans, thus causing difficulties to use such data for analysis. This paper presents an efficient approach to represent facial shapes from face scans through the reconstruction of face models based on regional information and a generic model. A hybrid approach using two vertex mapping algorithms, displacement mapping and point-to-surface mapping, and a regional blending algorithm are proposed to reconstruct the facial surface detail. The resulting models can represent individual facial shapes consistently and adaptively, establishing the facial point correspondence across individual models. The accuracy of the generated models is evaluated quantitatively. The applicability of the models is validated through the application for 3D facial expression recognition based on the databases of static 3DFE and dynamic 4DFE. A comparison with the state of the art has also been reported.