A Multi-label Convolutional Neural Network Approach to Cross-Domain Action Unit Detection

Action Unit (AU) detection from facial images is an important classification task in affective computing. However most existing approaches use carefully engineered feature extractors along with off-the-shelf classifiers. There has also been less focus on how well classifiers generalize when tested on different datasets. In our paper, we propose a multi-label convolutional neural network approach to learn a shared representation between multiple AUs directly from the input image. Experiments on three AU datasets-CK+, DISFA and BP4D indicate that our approach obtains competitive results on all datasets. Cross-dataset experiments also indicate that the network generalizes well to other datasets, even when under different training and testing conditions.