Deformable Synthesis Model for Emotion Recognition

摘要

In this paper, we propose a deformable synthesis model that can be used to synthesize data to train deep neural networks for the task of emotion recognition. This model is created through the use of 3D facial landmarks, which are then projected to the 2D image plane for training a deep network. We show that this model can accurately recognize a range of emotions that include happiness, sadness, and fear. We test the efficacy of our proposed approach on three publicly available 3D face databases, namely BU4DFE, BP4D, and BP4D+. We show that the proposed method can accurate recognize emotion when training and testing on the same database, as well as cross-database training and testing on all 3 databases. We show the proposed method results in accurate recognition of emotion using deep neural networks outperforming current state of the art on each of the tested databases.