HIGH-LEVEL FUZZY LINGUISTIC FEATURES OF FACIAL COMPONENTS IN HUMAN EMOTION RECOGNITION

  • Dewi Yanti Liliana Faculty of Computer Science, Universitas Indonesia, Indonesia
  • Tarzan Basaruddin Faculty of Computer Science, Universitas Indonesia, Indonesia
  • Muhammad Rahmat Widyanto Faculty of Computer Science, Universitas Indonesia, Indonesia
  • Imelda Ika Dian Oriza Faculty of Psychology, Universitas Indonesia, Indonesia

Abstract

Emotion is an important element in an interaction since it conveys Emotion is an important element in an interaction since it conveys human perception and response of an event. Unlike verbal words that can be manipulated, emotion is brief, spontaneous and provides more honest information. There are several classes of basic primary human emotions that differ from one another. These classes are happy, sad, fearful, surprised, disgusted, and angry. Meanwhile, a psychologist has developed a set of rules to recognize emotions based on facial expressions. This research aims to develop an artificial intelligent model based on psychological knowledge to recognize emotions by analyzing facial expressions. Moreover, the proposed model has defined high-level fuzzy linguistic features of facial components which distinguish it from existing methods that commonly use low-level image features (e.g. color, intensity, histogram, texture). High-level linguistic features (e.g. opened eyes, wrinkled nose) are better at representing human minds than low-level features which are only understood by machines. The model functions by detecting facial points first to locate important facial components; then extracting geometric facial components features which are then applied to a fuzzy facial components features which are then applied to a fuzzy facial components inference system resulting in high-level linguistic facial features. In the last step, the high-level linguistic features are applied to a fuzzy emotion inference system which classifies the input image into its respective emotion class based on psychological rules. Experiments conducted using facial expression dataset gave a high accuracy rate of 98.26% for fuzzy facial components linguistic identification. The proposed model also outperformed other classifiers (Fuzzy C-Means, Fuzzy Inference System, and Support Vector Machine). This intelligent model can contribute in various fields, including psychology, health, and education, especially in helping people with emotional disorders (e.g.Alexithymia, Asperger syndrome, and Autism) to recognize emotions.

 

Published
2020-02-06
How to Cite
LILIANA, Dewi Yanti et al. HIGH-LEVEL FUZZY LINGUISTIC FEATURES OF FACIAL COMPONENTS IN HUMAN EMOTION RECOGNITION. Journal of Information and Communication Technology, [S.l.], v. 19, n. 1, p. 103-129, feb. 2020. ISSN 2180-3862. Available at: <http://e-journal.uum.edu.my/index.php/jict/article/view/8619>. Date accessed: 29 feb. 2020.