Contrastive Representation Learning for Expression Recognition from Masked Face Images

With the worldwide spread of COVID-19, people are trying different ways to prevent the spread of the virus. One of the most useful and popular ways is wearing a face mask. Most people wear a face mask when they go out, which makes facial expression recognition become harder. Thus, how to improve the performance of the facial expression recognition model on masked faces is becoming an important issue. However, there is no public dataset that includes facial expressions with masks. Thus, we built two datasets which are a real-world masked facial expression database (VIP-DB) and a man-made masked facial expression database (M-RAF-DB). To reduce the influence of masks, we utilize contrastive representation learning and propose a two-branches network. We study the influence of contrastive learning on our two datasets. Results show that using contrastive representation learning improves the performance of expression recognition from masked face images.