Depression Detection by Facial Emotion Recognition Using Deep Neural Network

Abstract

Abstract: The task of facial emotion recognition is one of the widely used applications of image analysis as well as pattern recognition. In the biometric area of research, automatic face & facial expression recognition attracts researcher’s interest. For classifying facial expressions into different categories, it is necessary to extract important facial features that contribute to identifying proper and particular expressions. In this paper, a depression detection system is proposed. This paper will give an overview of the Depression Detection System using Facial Emotion Recognition techniques and datasets. Depression Detection systems based on facial gesture enable real-time analysis, tagging, and inference of cognitive affective states from a video recording of the face. It is assumed that facial expressions are triggered for a time period when an emotion is experienced, and so depression detection can be achieved by detecting the facial expression related to it. Out of all the major 6 emotions present, depression plays a vital role. Depression is classified as a mood disorder. It may be described as feelings of sadness, anger, or loss that interfere with a person’s everyday activities. Experimental results show that the scheme detects depression from real-time video capture from a camera with high accuracy.