Facial Expression Recognition with Data Augmentation and Compact Feature Learning

The convolutional neural network (CNN) based methods have made impressive progress in many computer vision tasks, such as object detection, face recognition, and so on. Their extraordinary capabilities are partially due to the exploration of the explosive growth of training set sizes. So those computer vision tasks with relatively small training sets available, like facial expression recognition, are still very challenging. In this work, we describe a practical data augmentation framework to synthesize large-scale training samples for the task of facial expression recognition in the wild. We also propose a new loss function, named cluster loss, to make deep features compact. Evaluated on a recent expression database RAF-DB, our method achieves better performance than state-of-the-art baselines and outperforms methods targeted on this database.