  As a fine-grained and local expression behavior measurement, facial action
unit (FAU) analysis (e.g., detection and intensity estimation) has been
documented for its time-consuming, labor-intensive, and error-prone annotation.
Thus a long-standing challenge of FAU analysis arises from the data scarcity of
manual annotations, limiting the generalization ability of trained models to a
large extent. Amounts of previous works have made efforts to alleviate this
issue via semi/weakly supervised methods and extra auxiliary information.
However, these methods still require domain knowledge and have not yet avoided
the high dependency on data annotation. This paper introduces a robust facial
representation model MAE-Face for AU analysis. Using masked autoencoding as the
self-supervised pre-training approach, MAE-Face first learns a high-capacity
model from a feasible collection of face images without additional data
annotations. Then after being fine-tuned on AU datasets, MAE-Face exhibits
convincing performance for both AU detection and AU intensity estimation,
achieving a new state-of-the-art on nearly all the evaluation results. Further
investigation shows that MAE-Face achieves decent performance even when
fine-tuned on only 1\% of the AU training set, strongly proving its robustness
and generalization performance.
