  Automatic recognition of facial gestures is becoming increasingly important
as real world AI agents become a reality. In this paper, we present an
automated system that recognizes facial gestures by capturing local changes and
encoding the motion into a histogram of frequencies. We evaluate the proposed
method by demonstrating its effectiveness on spontaneous face action
benchmarks: the FEEDTUM dataset, the Pain dataset and the HMDB51 dataset. The
results show that, compared to known methods, the new encoding methods
significantly improve the recognition accuracy and the robustness of analysis
for a variety of applications.
