In this paper, we describe the use of Delaunay triangulation to blend images of faces, which allows us to create and automatically label facial expressions portraying varying intensities of emotion. We have applied this technique on the RafD dataset consisting of 67 participants and 8 categorical emotions and evaluated the augmentation in two ways { using a facial expression generation and recognition tasks with deep learning models. For the generation task, we used a deconvolution neural network which learns to encode the input images in a high-dimensional feature space and generate realistic expressions at varying intensities. The augmentation significantly improves the quality of images compared to previous comparable experiments and it allows to create images with a higher resolution. For the recognition task, we evaluated pre-trained Densenet121 and Resnet50 networks with either the original or augmented dataset. Our results indicate that the augmentation alone has a similar or better performance compared to the original. Implications of this method and its role in improving existing facial expression generation and recognition approaches are discussed.
