Abstract:
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. This
work aims to: (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. This optical flow is used to train the FlowNetS architecture to test its performance on the
Extended Cohn-Kanade dataset and a portion of the generated dataset. The performance of FlowNetS trained on face images
surpassed that of other optical flow CNN architectures. 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.
