FG-Net: Facial Action Unit Detection with Generalizable Pyramidal
  Features
Yufeng Yin Di Chang Guoxian Song Shen Sang Tiancheng Zhi Jing Liu Linjie Luo Mohammad Soleymani
Automatic detection of facial Action Units (AUs) allows for objective facial
expression analysis. Due to the high cost of AU labeling and the limited size
of existing benchmarks, previous AU detection methods tend to overfit the
dataset, resulting in a significant performance loss when evaluated across
corpora. To address this problem, we propose FG-Net for generalizable facial
action unit detection. Specifically, FG-Net extracts feature maps from a
StyleGAN2 model pre-trained on a large and diverse face image dataset. Then,
these features are used to detect AUs with a Pyramid CNN Interpreter, making
the training efficient and capturing essential local features. The proposed
FG-Net achieves a strong generalization ability for heatmap-based AU detection
thanks to the generalizable and semantic-rich features extracted from the
pre-trained generative model. Extensive experiments are conducted to evaluate
within- and cross-corpus AU detection with the widely-used DISFA and BP4D
datasets. Compared with the state-of-the-art, the proposed method achieves
superior cross-domain performance while maintaining competitive within-domain
performance. In addition, FG-Net is data-efficient and achieves competitive
performance even when trained on 1000 samples. Our code will be released at
\url{https://github.com/ihp-lab/FG-Net}