Recognition of Depression from Video Frames by using Convolutional Neural Networks

The disturbances of the mood are relevant to the emotions. Specifically, the behaviour of persons with disturbances of mood, like the depression of the unipolar, displays a powerful correlation of the temporal by the emotional girths of the arousal and the valence. Moreover, the psychiatrists and the psychologists take into account the audible signs of the facial and the audible signs of the voice when they assess the condition of the patient. Depression makes audible behaviours like weak expressions, the validation of the contact of the eye and the use of little flat-voiced sentences. Artificial intelligence has combined various automated frameworks for the detection of depression severity by using hand-crafted features. The method of deep learning has been successfully applied to detect depression. In the current article, a federate architecture, which is the network of the neural of the deep convolutional basis on the attention of global, is proposed to diagnose the depression. This method uses CNN with the attention mechanism and also uses the integration of the weighted spatial pyramid pooling for the learning of the deep global representation. In this method, two branches are introduced: the CNN based on local attention focuses on the patches of the local, while the CNN based on global attention attains the universal patterns from the whole face area. For taking the data of the supplementary among two parts, a CNN basis on the local-global attention is proposed. The designed experiments have been done in two datasets, which are AVEC2014 and AVEC2013. The results show that our presented approach can extract the depression patterns from the video frames. Also, the outcomes display that our presented approach is superior to the best methods based on the video for the detection of depression.