  Automatic facial behavior analysis has a long history of studies in the
intersection of computer vision, physiology and psychology. However it is only
recently, with the collection of large-scale datasets and powerful machine
learning methods such as deep neural networks, that automatic facial behavior
analysis started to thrive. Three of its iconic tasks are automatic recognition
of basic expressions (e.g. happy, sad, surprised), estimation of continuous
emotions (e.g., valence and arousal), and detection of facial action units
(activations of e.g. upper/inner eyebrows, nose wrinkles). Up until now these
tasks have been mostly studied independently collecting a dataset for the task.
We present the first and the largest study of all facial behaviour tasks
learned jointly in a single multi-task, multi-domain and multi-label network,
which we call FaceBehaviorNet. For this we utilize all publicly available
datasets in the community (around 5M images) that study facial behaviour tasks
in-the-wild. We demonstrate that training jointly an end-to-end network for all
tasks has consistently better performance than training each of the single-task
networks. Furthermore, we propose two simple strategies for coupling the tasks
during training, co-annotation and distribution matching, and show the
advantages of this approach. Finally we show that FaceBehaviorNet has learned
features that encapsulate all aspects of facial behaviour, and can be
successfully applied to perform tasks (compound emotion recognition) beyond the
ones that it has been trained in a zero- and few-shot learning setting.
