Data Augmentation using non-rigid CPD Registration for 3D Facial Expression Recognition

3D Facial Expression Recognition (FER) is an active research topic due to its multi-fields human machine applications. We expose in this paper a new approach for Data Augmentation (DA) in order to improve 3D FER using Deep Neural Networks (DNN). Our main contribution consists in using the Coherent Point Drift (CPD) non-rigid registration to generate additional 3D facial data conveying various expressions mainly the prototypical expressions: Happiness, Sadness, Fear, Surprise, Disgust, and Anger. We start by choosing a set of different references defined by arbitrarily selected neutral faces. We apply then the CPD non-rigid registration between each selected neutral face and each 3D facial model conveying various expressions from the whole BU-3DFE database. Thus, we augment the dataset by a factor equal to the used references. Afterwards, we estimate the 3D elastic deformation between the reference (3D neutral face) and the target (3D face with expression) in order to generate consequently various 3D expressions by switching the reference and the target within the registration process. Afterwards, we gather the produced 3D expressions to increase the size of our dataset. Finally, we exploit a DNN architecture to evaluate our proposed DA method. The used DA is effective and increases our DNN performance. Experimental results operated on the whole BU-3DFE database shows promising results reaching 94.88%.