Automatic Mood and Gloom Detection using Visual Inputs

Abstract

In natural psychological equilibrium, tension may be generally perceived as disturbance. If a user is unable to reconcile the expectations imposed on him/ her with user capacity to deal to them, so it generates tension and produces burden on mental health. Gloom may be generally described as psychological equilibrium disruption. One of major research fields of biomedical engineering is Gloom detection, as proper Gloom prevention could be easy. Facial expression recognition is the process of identifying human emotion. This is both something that humans do automatically but computational methodologies have also been developed. Several bio signals are available. Which are useful in identifying levels of Mood and Gloom since these signals indicate distinctive changes in the induction of Mood and Gloom. In this project, because of the easily accessible datasets on Kaggle, image processing is used as the primary candidate and the CNN model types have been formed which is used to predict the mood and gloom of persons.