In computer vision, occlusions are mainly known as a challenge to cope with. For instance, partial occlusions of the face may lower the performance of facial expression recognition systems. However, when incorporated into the training, occlusions can be also helpful in improving the overall performance. In this paper, we propose and evaluate occlusion augmentation as a simple but effective regularizing tool for improving the general performance of deep learning based facial expression and action unit recognition systems, even if no occlusion is present in the test data. In our experiments we consistently found significant performance improvements on three databases (Bosphorus, RAF-DB, and AffectNet) and three CNN architectures (Xception, MobileNet, and a custom model), suggesting that occlusion regularization works independently of the dataset and architecture. Based on our clear results, we strongly recommend to integrate occlusion regularization into the training of all CNN-based facial expression recognition systems, because it promises performance gains at very low cost.