Exploring Facial Biomarkers for Depression through Temporal Analysis of Action Units

TL;DR

This study explores the use of facial action units and emotion analysis as objective biomarkers for depression, utilizing video data and advanced statistical methods to distinguish depressed individuals from non-depressed controls.

Contribution

It introduces a novel approach combining facial action unit analysis, PCA, and clustering to identify depression-related facial expression patterns.

Findings

Significant differences in sadness and happiness-related AUs between groups

Facial expression patterns can differentiate depressed from non-depressed individuals

Time series models effectively classify depression based on facial features

Abstract

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.