  Computing optical flow is a fundamental problem in computer vision. However,
deep learning-based optical flow techniques do not perform well for non-rigid
movements such as those found in faces, primarily due to lack of the training
data representing the fine facial motion. We hypothesize that learning optical
flow on face motion data will improve the quality of predicted flow on faces.
The aim of this work is threefold: (1) exploring self-supervised techniques to
generate optical flow ground truth for face images; (2) computing baseline
results on the effects of using face data to train Convolutional Neural
Networks (CNN) for predicting optical flow; and (3) using the learned optical
flow in micro-expression recognition to demonstrate its effectiveness. We
generate optical flow ground truth using facial key-points in the
BP4D-Spontaneous dataset. The generated optical flow is used to train the
FlowNetS architecture to test its performance on the generated dataset. The
performance of FlowNetS trained on face images surpassed that of other optical
flow CNN architectures, demonstrating its usefulness. Our optical flow features
are further compared with other methods using the STSTNet micro-expression
classifier, and the results indicate that the optical flow obtained using this
work has promising applications in facial expression analysis.
