Point-Based Deep Neural Network for 3D Facial Expression Recognition

摘要

3D data are an important resource for many computer-based applications, as they provide valuable depth cues about the full geometry of 3D associated objects. They become even more valuable as regards 3D face/ facial expression recognition using deep learning. Indeed, two main challenges remain under study. The first is how to resume 3D faces with a discriminative representation from a 3D point cloud while exploiting an adequate Deep Neural Network (DNN). The second is the lack of large 3D facial datasets. To address the first issue, we propose to exploit solely geometric information while applying DNN. Hence, in order to deal with high resolution face scans with a rich point cloud representation, we extract point-based representations using various sampling strategies. Different keypoint sets are used, ranging from a small set of points of interest (i.e. landmarks) to point sets sampled from a curve-based representation, as well as scale-invariant feature transform keypoints.As for the second issue and in order to overcome overfitting caused mainly by the lack of large labelled datasets while applying DNN, we propose to generate new realistic-like facial expressions using non-rigid registration techniques. The effectiveness of the suggested approach is demonstrated through conducting experiments on the BU-3DFE database. The quantitative evaluation and comparison with the recently developed state of the art show the competitiveness of the proposed 3D facial expression recognition approach.