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

Modeling Spatial and Temporal Cues for Multi-label Facial Action Unit Detection