Exploiting Facial Action Unit in Video for Recognizing Depression using Metaheuristic and Neural Networks

Abstract

The ubiquity of coronavirus cases around the world has been severe and its impact is not only affecting the economy and physical health, but also mental health such as depression. Unfortunately, the number of coronavirus cases may inhibit people to look for general practitioners or hospitals. This study represents research on facial behaviour analysis on recognizing depression from facial action units extracted from images or videos. We aimed to find a reduced set of facial action unit features using the metaheuristic approach. We utilized particle swarm optimization to select the best predictors and feed them to optimized standard feedforward neural networks. We obtained 97.83% accuracy for depression detection based on Distress Analysis Interview Corpus Wizard-of-Oz (DAIC WOZ) database containing 189 video sessions associated with the Patient Health Questionnaire depression label. This level of accuracy requires almost 9 minutes. However, this level of accuracy is higher than other state-of-the-art methods.