Facial Action Unit Detection using 3D Facial Landmarks
This work addresses emotion analysis in computer vision by providing a more accurate method for facial action unit detection, though it appears incremental as it builds on existing CNN and landmark techniques.
The paper tackles facial action unit detection by training a 2D CNN on 3D facial landmarks, achieving improved results over state-of-the-art methods on BP4D and BP4D+ datasets, with detection on over 330,000 frames.
In this paper, we propose to detect facial action units (AU) using 3D facial landmarks. Specifically, we train a 2D convolutional neural network (CNN) on 3D facial landmarks, tracked using a shape index-based statistical shape model, for binary and multi-class AU detection. We show that the proposed approach is able to accurately model AU occurrences, as the movement of the facial landmarks corresponds directly to the movement of the AUs. By training a CNN on 3D landmarks, we can achieve accurate AU detection on two state-of-the-art emotion datasets, namely BP4D and BP4D+. Using the proposed method, we detect multiple AUs on over 330,000 frames, reporting improved results over state-of-the-art methods.