Real-time Action Unit Intensity Detection

Abstract

We present a real-time system for action unit intensity detection. We train a convolutional neural network, with images from DISFA+, to detect the intensities of 12 action units that are commonly found in the literature. Along with real-time capabilities, the system is also able to detect intensities on static images, and in the wild videos. While the focus of this work is on the detection of action unit intensities, we are able to implicitly detect the occurrence of them as well. This is done by setting all action units with an intensity greater than 0 as active. In doing this, we calculate the F1-micro and F1-binary scores for the DISFA+ dataset.