Machine Learning Analysis of Anxiety and Depression in children

Abstract: Psychological issues among students, including as depression and anxiety, are mostly caused by a failure to check students' psychological well-being on a regular basis. If depression is discovered early enough, it can be treated successfully. Image processing advancements have resulted in the development of successful algorithms that can identify emotions from facial pictures in a much more basic manner. As a result, for successful diagnosis of depression, we require an automated system that records and analyses student facial photos. In the proposed system, image processing techniques are used to analyse student facial features and depression can be predicted. To predict depression, a video of the student is gathered, and the student's face is identified by use of haarcascade classifier. The Mini Xception classifier is used to classify these facial traits. After quantifying the system, we will be able to implant it in mobile devices in the future.