Depression level assessment based on 3D CNN and facial expression videos

Depression seriously affects People's Daily life and work, and may even lead to suicide. The method based on deep learn ing is expected to assist the clinical diagnosis of depression more effectively and objectively. Usually, 2D convolutional neural network (CNN) is used for feature extraction for depression level assessment, but this method can only extract stat ic features and will lose dynamic spatiotemporal features. In contrast, 3D CNN model can directly extract temporal and s patial features, which can improve the performance of depression level assessment. In this paper, in order to compare the depression level assessment performance of different 3D convolutional neural networks, we conducted tests using 3D V GGNet, 3D GooleNet, 3D ResNet, 3D SENet and 3D DenseNet networks based on AVEC2013 and AVEC2014 datasets. The experimental results show that the 3D ResNet18 network obtains the best evaluation results,MAE=7.48,RMSE=9.7 0 on the AVEC2013 dataset,MAE= 7.03,RMSE=9.01 on the AVEC2014 dataset. Compared to other existing methods, 3 D ResNet18 shows excellent performance.