Facial action unit detection is an ongoing research area in image processing and computer vision. Different methods have been proposed in the recent years for accurate detection of action units in facial images for various views and lighting conditions. In this paper, we propose a new deep learning scheme that integrates convolutional neural networks with association rule mining. Convolutional neural network is used for automatic extraction of features from input images, and the results are exploited to build two different association rule mining-based classifiers. The obtained results show an improvement compared to a fully connected neural network, for which the features are originally designed, for some action units.