A Content-aware Filtering for RGBD Faces

3D face models are widely used for several purposes, such as biometric systems, face verification, facial expression recognition, 3D visualization, and so on. They can be captured by using different kinds of devices, like plenoptic cameras, structured light cameras, time of flight, among others. Nevertheless, the models generated by all these consumer devices are quite noisy. In this work, we present a content-aware filtering for 2.5D meshes of faces that preserves their intrinsic features. This filter consists on an exemplar-based neighborhood matching where all models are in a frontal position avoiding rotation and perspective. We take advantage of prior knowledge of the models (faces) to improve the comparison. We first detect facial feature points, create the point correctors for regions of each feature, and only use the correspondent regions for correcting a point of the filtered mesh. The model is invariant to depth translation and scale. The proposed method is evaluated on a public 3D face dataset with different levels of noise. The results show that the method is able to remove noise without smoothing the sharp features of the face.