Emotion Detection using Facial Image for Behavioral Analysis

The Treatment of mental disease is unique in that it relies on subjective evaluation. Another problem with mental disease diagnosis standards is the lack of consideration for individual departures from the broad categories of symptoms. The rising availability of sensor-equipped portable digital devices, such as smartphones, opens up new possibilities for monitoring human activity in real time made the task easier. Individuals’ mental health and well-being can be better assessed as a result of this more exact method. More exact and tailored digital treatments in mental health care may be made possible by better modeling of individual mental health and by taking into account the context of the individual, which can be monitored through continuous monitoring. Such strategies for measuring, modeling, and treating mental illness and sustaining mental health became quite possible now. This Project proposes to apply various algorithms such as Decision tree, Random Forest, ANN to detect the mental health using certain facial emotion factors either from getting the input from webcam or uploading the facial image of the individuals.