  Depression, a prevalent and complex mental health issue affecting millions
worldwide, presents significant challenges for detection and monitoring. While
facial expressions have shown promise in laboratory settings for identifying
depression, their potential in real-world applications remains largely
unexplored due to the difficulties in developing efficient mobile systems. In
this study, we aim to introduce FacePsy, an open-source mobile sensing system
designed to capture affective inferences by analyzing sophisticated features
and generating real-time data on facial behavior landmarks, eye movements, and
head gestures -- all within the naturalistic context of smartphone usage with
25 participants. Through rigorous development, testing, and optimization, we
identified eye-open states, head gestures, smile expressions, and specific
Action Units (2, 6, 7, 12, 15, and 17) as significant indicators of depressive
episodes (AUROC=81%). Our regression model predicting PHQ-9 scores achieved
moderate accuracy, with a Mean Absolute Error of 3.08. Our findings offer
valuable insights and implications for enhancing deployable and usable mobile
affective sensing systems, ultimately improving mental health monitoring,
prediction, and just-in-time adaptive interventions for researchers and
developers in healthcare.
