Depression Detection via Facial Expressions and Movement Analysis Using Machine Learning with Optimized Feature Selection

Abstract

Mental health disorders affect millions of people globally, posing considerable challenges for the detection and monitoring of depression. In this study, we aim to introduce several feature selection and machine learning approaches to optimize the data on facial behavior AUs, probabilities classification, head Euler angles, and facial landmarks with 25 participants. We computed statistical features for AUs, probabilities classification, head Euler angles, and facial landmarks such as min, max, mean, median, sum, and standard deviation of each facial data point from 25 participants for detection value data from PHQ-9 label depression episode between 0 and 1. Therefore, we achieve significant indicators of depressive episodes, achieving 0.94% of AUROC with the model SVC approach, 0.92% of AUROC with the model random forest classifier approach, 0.91% of AUROC with the model XGBClassifier approach, and 0.91% of AUROC with the model ANN machine learning approach, respectively.