Depression Diagnosis Algorithm Based on 2-stream CNN Using Facial Image

In this study, a 2-stream CNN model was proposed to improve depression prediction accuracy by analyzing facial images. For image pre-processing, the AVEC2014 database was used to cut the participant's eyebrows, eyes, nasolabial folds, mouth, and tail to create images and use them as input data. It also computes and visualizes the optical flow between successive frames. It is expected that various depression patterns can be analyzed with high accuracy by predicting depression scores using a proposed model combining ResNet and Squeeze and Excitation Networks (SENet).