Facial Emotion Recognition In Real-time Using Deep Convolution Networks

Abstract

The objective of this study is to present a detection method associated to automatic live facial expression identification by analysing the frontal-face image and predicting the most accurate expression out of the seven major countenances. Since our behaviour is strongly correlated to our emotions, the facial expressions and body gestures may act as a noteworthy source of non-verbal communication that may tell about the state of an individual. The interest in emotional computing through facial expressions has increased as it has wide application in industries, market and medical field. The doctors may be helped through facial expression recognition, may be by online machine monitoring system or sometimes when any patient is not able to communicate verbally. This study presents a model for facial recognition that permits disturbances to apprehend information from their surroundings in real-time, thus improving the classification process. The work has been implemented using Python IDLE (3.7) and Open Source Computer Vision Library (Open-CV2). The study proves to provide accurate and precise results.