  Depression is characterized by persistent sadness and loss of interest,
significantly impairing daily functioning and now a widespread mental disorder.
Traditional diagnostic methods rely on subjective assessments, necessitating
objective approaches for accurate diagnosis. Our study investigates the use of
facial action units (AUs) and emotions as biomarkers for depression. We
analyzed facial expressions from video data of participants classified with or
without depression. Our methodology involved detailed feature extraction, mean
intensity comparisons of key AUs, and the application of time series
classification models. Furthermore, we employed Principal Component Analysis
(PCA) and various clustering algorithms to explore the variability in emotional
expression patterns. Results indicate significant differences in the
intensities of AUs associated with sadness and happiness between the groups,
highlighting the potential of facial analysis in depression assessment.
