Detection of stress, anxiety and depression (SAD) in video surveillance using ResNet-101

Abstract

Emotional disruptions are associated with the psychological state of a person that comes out in the form of non-verbal signals. The usage of medical resources for the identification of emotional activities is a complex and expensive task. Computer vision techniques equipped with artificial intelligence are capable of bringing automatic and fast identification of emotional variations of the human mind. Emotional variations may contain overlapping stages in which multiple non-separable emotional symptoms are more difficult to classify. The objective is to draw up an investigation of such a non-verbal body signal and correlate it with the psychological state of the person. Artificial intelligence techniques explore the identification of psychological states using pixel intensity information from datasets of facial expressions. The proposed study explores the classification of emotional symptoms into stress, anxiety and depression from facial expressions in a real-time video surveillance dataset. The second objective of the proposed study is to maintain classification accuracy for variation of real-time noise that may distort feature information. The study exhibits the use of the Kalman filter for the localization of intensity-based features and the use of the bilateral filter, contrast enhancement and adaptive filter algorithms for the removal of noise. Finally, ResNet 101 architecture has been used to classify symptoms of stress, anxiety and depression. The robustness of the proposed classification algorithm has been compared with other algorithms, such as PCA, Gradient boosting algorithm, KNN, Decision tree, Naïve Bayes, and SVM. It has been observed that ResNet 101 outperformed other models with a notable 98.4% accuracy.