Real Time Facial Expression Recognition System Based on Deep Learning
Jose Carlos Bustamante1, Ciro Rodriguez2, Doris Esenarro3
1Jose Carlos Bustamante, National University of San Marcos, Peru, South America.
2Ciro Rodriguez, National University of San Marcos, Peru, South America.
3Doris Esenarro, National University Federico Villarreal, Peru, South America.
Manuscript received on 21 October 2019 | Revised Manuscript received on 25 October 2019 | Manuscript Published on 02 November 2019 | PP: 4047-4051 | Volume-8 Issue-2S11 September 2019 | Retrieval Number: B15910982S1119/2019©BEIESP | DOI: 10.35940/ijrte.B1591.0982S1119
Open Access | Editorial and Publishing Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The automatic detection of facial expressions is an active research topic, since its wide fields of applications in human-computer interaction, games, security or education. However, the latest studies have been made in controlled laboratory environments, which is not according to real world scenarios. For that reason, a real time Facial Expression Recognition System (FERS) is proposed in this paper, in which a deep learning approach is applied to enhance the detection of six basic emotions: happiness, sadness, anger, disgust, fear and surprise in a real-time video streaming. This system is composed of three main components: face detection, face preparation and face expression classification. The results of proposed FERS achieve a 65% of accuracy, trained over 35558 face images.
Keywords: Real Time, Affective Computing, Facial Expression Recognition System, Deep Learning, Emotion Classification, Convolutional Neural Networks.
Scope of the Article: Deep Learning