Face Emotion Recognition System for Depression Detection Using AI Techniques

Abstract

Abstract: The human face is an essential aspect of an individual's body. It plays a crucial function in detecting and identifying emotions since the face is where a person exhibits all of their fundamental emotions. Through human emotions, we solve different types of problems. Like healthcare, security, business, and education. The purpose of this paper is to present the detection of depression in the mental health sector. Depression or stress is faced by most of the population all over the world for many reasons and at different stages of life. As the current human life is a busy life cycle, a person gets depressed or stressed in their daily life. Depression may be found in educational activities, competitive or challenging tasks, employment pressure, family consequences, different sorts of human connection management, health issues, old age, and other situations. Artificial intelligence and deep learning approaches are suggested in this study to assess depression. This research is useful for analyzing the mental health of every employer and psychologist when counselling their patients. Here, we propose a deep convolutional neural network (DCNN) model. This model can classify two types of human facial emotions. Which is based on positive and negative emotions. This model is trained and tested using the FER-2013 dataset. The data set used for experimentation is a FER (Facial Expression Recognition) dataset available in the KAGGLE repository. The implementation environment includes Keras, TensorFlow, and OpenCV Python packages. The result includes the emotion detection accuracy between the training and test phases. The average accuracy achieved was 77%.