Deep 3D-CNN for Depression Diagnosis with Facial Video Recording of Self-Rating Depression Scale Questionnaire

TL;DR

This paper presents a deep 3D-CNN system that analyzes facial videos recorded during self-assessment to improve the accuracy of depression diagnosis from the Self-Rating Depression Scale questionnaire.

Contribution

It introduces an end-to-end deep learning approach that combines facial video analysis with questionnaire data for more reliable self-diagnosis of depression.

Findings

The system outperforms existing methods in depression detection accuracy.

Facial expressions during self-assessment provide valuable diagnostic information.

Combining video analysis with questionnaire scores enhances self-diagnosis validity.

Abstract

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.