Authors: Meenakshi Kumar; Tanya Goyal; Pankaj Gupta
Addresses: Department of Electronics and Communication Engineering, Indira Gandhi Delhi Technical University for Women, New Delhi, India ' Department of Electronics and Communication Engineering, Indira Gandhi Delhi Technical University for Women, New Delhi, India ' Department of Electronics and Communication Engineering, Indira Gandhi Delhi Technical University for Women, New Delhi, India
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.
Keywords: emotion classification; facial landmark detection; 3D facial features recognition; multi-modal sentiment analysis; face detection; feature extraction; tensor flow.
International Journal of Behavioural and Healthcare Research, 2021 Vol.7 No.3, pp.187 - 199
Received: 19 Jun 2020
Accepted: 23 Dec 2020
Published online: 22 Apr 2021 *