Automated detection of human mental disorder

Abstract

The pressures of daily life result in a proliferation of terms such as stress, anxiety, and mood swings. These feelings may be developed to depression and more complicated mental problems. Unfortunately, the mood and emotional changes are difficult to notice and considered a disease that must be treated until late. The late diagnosis appears in suicidal intensions and harmful behaviors. In this work, main human observable facial behaviors are detected and classified by a model that has developed to assess a person’s mental health. Haar feature-based cascade is used to extract the features from the detected faces from FER+ dataset. VGG model classifies if the user is normal or abnormal. Then in the case of abnormal, the model predicts if he has depression, anxiety, or other disorder according to the detected facial expression. The required assistance and support can be provided in a timely manner with this prediction. The system has achieved a 95% of overall prediction accuracy.