A Scalable Approach for Facial Action Unit Classifier Training UsingNoisy Data for Pre-Training

To present a large set of automatically FACS-annotated images with gender, nationality and biographical meta-data. To propose a simple pipeline of pre-training and fine-tuning a CNN classifier in an end-to-end fashion for detecting the presence of facial action units that produces state-of-the-art performance. To conduct experiments to systematically investigate the effect of (1) the number of pre-training images and (2) the number of pre-training images of different people.