Real time facial expression recognition system based on deep learning

Jose Carlos Bustamante, Ciro Rodriguez Rodriguez, Doris Esenarro

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

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..

Original languageEnglish
Pages (from-to)4047-4051
Number of pages5
JournalInternational Journal of Recent Technology and Engineering
Volume8
Issue number2 Special Issue 11
DOIs
StatePublished - Sep 2019
Externally publishedYes

Keywords

  • Affective Computing
  • Convolutional Neural Networks
  • Deep Learning
  • Emotion Classification
  • Facial Expression Recognition System
  • Real Time

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