Abstract
Depression is a significant global health burden, affecting over 322 million people worldwide and projected to surpass cardiovascular disease as the leading cause of disability by 2030. Despite advancements in mental health services, accurate and accessible diagnostic methods remain a critical challenge. Traditional approaches, such as psychiatric consultations, face limitations due to the physician-patient ratio and reliance on subjective self-report scales, which can lead to inaccuracies. Recent research has explored alternative methods, including facial behavior analysis, for objective depression assessment. This approach is cost-effective, non-invasive, and suitable for real-world applications. This study builds upon existing research, such as FacePsy and MoodCapture, by introducing a temporalaware ensemble learning framework that enhances depression assessment by integrating multiple models to capture both static and dynamic facial behavior patterns. Through comprehensive experiments, we demonstrate that models trained on specific facial features, such as Eyes Open and Smiling Probability, achieve superior performance, with F1 Scores ranging from 0.7093 to 0.7388, accuracy from 0.6607 to 0.7058, and AUROC from 0.7432 to 0.7845. In contrast, models trained on full feature sets exhibit lower performance, highlighting the importance of effective feature selection and pre-processing. The incorporation of temporal modeling further refines depression detection by capturing subtle facial dynamics that static models may overlook. This study bridges theoretical research with practical applications, fostering the development of innovative solutions for addressing the mental health crisis.
https://ror.org/0004wsx81 
