Emotion Detection and Suicidal Intention Prediction of Differently Depressed Individuals Using Machine Learning Techniques

Facial expressions play an important role in conveying emotions, especially in human-machine interaction. Automatic facial expression recognition (FER) systems have numerous potential applications, such as detecting mental disorders, understanding human behavior, and generating synthetic human expressions. However, achieving high recognition rates remains a challenging task. In the literature, two popular approaches for automatic FER are based on geometry and appearance. The FER process typically consists of four stages, namely pre-processing, face detection, feature extraction, and expression classification. In our project, we utilized various deep learning techniques, specifically convolutional neural networks, to detect seven essential human emotions: anger, disgust, fear, happiness, sadness, surprise, and neutrality. Furthermore, our aim was to predict suicidal tendencies based on the detected emotions since depression is the primary cause of suicide. Detecting emotions in depressed individuals could facilitate their monitoring and help prevent suicide risk by forecasting the rate of suicidal intentions based on their emotional state.