  Facial action units (FAUs) are critical for fine-grained facial expression
analysis. Although FAU detection has been actively studied using ideally high
quality images, it was not thoroughly studied under heavily occluded
conditions. In this paper, we propose the first occlusion-robust FAU
recognition method to maintain FAU detection performance under heavy
occlusions. Our novel approach takes advantage of rich information from the
latent space of masked autoencoder (MAE) and transforms it into FAU features.
Bypassing the occlusion reconstruction step, our model efficiently extracts FAU
features of occluded faces by mining the latent space of a pretrained masked
autoencoder. Both node and edge-level knowledge distillation are also employed
to guide our model to find a mapping between latent space vectors and FAU
features. Facial occlusion conditions, including random small patches and large
blocks, are thoroughly studied. Experimental results on BP4D and DISFA datasets
show that our method can achieve state-of-the-art performances under the
studied facial occlusion, significantly outperforming existing baseline
methods. In particular, even under heavy occlusion, the proposed method can
achieve comparable performance as state-of-the-art methods under normal
conditions.
