Face Detection Approach to Classify Emotions Based on Facial Expression in Depressive Disorder

Abstract

The Multi-Task Cascaded Convolution Neural Network (MTCNN) is presented in this paper to classify the emotion and generate the facial dots as a representative of the patient. In depressive disorder diagnosis, the facial expressions can be used to observe the behavior of the patient. From the results, the system can be classified the emotion into 5-class i.e., happy, angry, disgusted, neutral, and surprised. For emotions of happy, angry, neutral, and surprised, the accuracy is more than 98 %, and for disgusted emotion is 96 %. Furthermore, the system can generate the real-time facial dots for emotion classification. Therefore, it can be a candidate to apply to collect and analyze the emotions of the patient under the privacy policy in a depressive disorder.