Deep 3D-CNN for Depression Diagnosis with Facial Video Recording of Self-Rating Depression Scale Questionnaire
This work addresses the issue of inaccurate self-diagnosis in mental health screening, though it is incremental as it builds on existing methods for temporal modeling in video analysis.
The paper tackled the problem of unreliable self-administered depression screening by proposing a system that combines facial video recordings with questionnaire responses to improve accuracy, achieving superior performance in interpreting depression from a dataset of 200 participants.
The Self-Rating Depression Scale (SDS) questionnaire is commonly utilized for effective depression preliminary screening. The uncontrolled self-administered measure, on the other hand, maybe readily influenced by insouciant or dishonest responses, yielding different findings from the clinician-administered diagnostic. Facial expression (FE) and behaviors are important in clinician-administered assessments, but they are underappreciated in self-administered evaluations. We use a new dataset of 200 participants to demonstrate the validity of self-rating questionnaires and their accompanying question-by-question video recordings in this study. We offer an end-to-end system to handle the face video recording that is conditioned on the questionnaire answers and the responding time to automatically interpret sadness from the SDS assessment and the associated video. We modified a 3D-CNN for temporal feature extraction and compared various state-of-the-art temporal modeling techniques. The superior performance of our system shows the validity of combining facial video recording with the SDS score for more accurate self-diagnose.