  Critical obstacles in training classifiers to detect facial actions are the
limited sizes of annotated video databases and the relatively low frequencies
of occurrence of many actions. To address these problems, we propose an
approach that makes use of facial expression generation. Our approach
reconstructs the 3D shape of the face from each video frame, aligns the 3D mesh
to a canonical view, and then trains a GAN-based network to synthesize novel
images with facial action units of interest. To evaluate this approach, a deep
neural network was trained on two separate datasets: One network was trained on
video of synthesized facial expressions generated from FERA17; the other
network was trained on unaltered video from the same database. Both networks
used the same train and validation partitions and were tested on the test
partition of actual video from FERA17. The network trained on synthesized
facial expressions outperformed the one trained on actual facial expressions
and surpassed current state-of-the-art approaches.
