Robust features for facial action recognition
This work addresses the problem of robust facial gesture recognition for real-world AI agents, though it appears incremental as it builds on existing encoding methods.
The paper tackles facial action recognition by developing an automated system that encodes local motion changes into frequency histograms, demonstrating significant improvements in recognition accuracy and robustness across three spontaneous face action benchmarks (FEEDTUM, Pain, and HMDB51 datasets).
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.