  Facial action units (AUs) are essential to decode human facial expressions.
Researchers have focused on training AU detectors with a variety of features
and classifiers. However, several issues remain. These are spatial
representation, temporal modeling, and AU correlation. Unlike most studies that
tackle these issues separately, we propose a hybrid network architecture to
jointly address them. Specifically, spatial representations are extracted by a
Convolutional Neural Network (CNN), which, as analyzed in this paper, is able
to reduce person-specific biases caused by hand-crafted features (eg, SIFT and
Gabor). To model temporal dependencies, Long Short-Term Memory (LSTMs) are
stacked on top of these representations, regardless of the lengths of input
videos. The outputs of CNNs and LSTMs are further aggregated into a fusion
network to produce per-frame predictions of 12 AUs. Our network naturally
addresses the three issues, and leads to superior performance compared to
existing methods that consider these issues independently. Extensive
experiments were conducted on two large spontaneous datasets, GFT and BP4D,
containing more than 400,000 frames coded with 12 AUs. On both datasets, we
report significant improvement over a standard multi-label CNN and
feature-based state-of-the-art. Finally, we provide visualization of the
learned AU models, which, to our best knowledge, reveal how machines see facial
AUs for the first time.
