Human emotion recognition based on active appearance model and semi-supervised fuzzy C-means

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

15 Citations (Scopus)

Abstract

Human emotion recognition is an emerging research area in the field of social signal processing. Facial expression is an important means to detect human emotion. The problem is some facial expressions represent similar emotions. Thus, the recognition must consider the ambiguity in the way human expresses emotions through face. Existing methods do not take into account the level of expression's ambiguity. In our research, we specify face points and display the degree of fuzzy cluster on eight face emotions, namely anger, contempt, disgust, happy, surprise, sadness, fear, and neutral. The proposed methods are based on Active Appearance Model (AAM) and semi-supervised Fuzzy C-means (FCM). We tested the system on Cohn Kanade+ dataset of facial expression which provided eight classes of human emotion. Our methods gain an average accuracy rate of 80.71% and surpass the existing Fuzzy Inference System.

Original languageEnglish
Title of host publication2016 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages439-445
Number of pages7
ISBN (Electronic)9781509046294
DOIs
Publication statusPublished - 6 Mar 2017
Event8th International Conference on Advanced Computer Science and Information Systems, ICACSIS 2016 - Malang, Indonesia
Duration: 15 Oct 201616 Oct 2016

Publication series

Name2016 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2016

Conference

Conference8th International Conference on Advanced Computer Science and Information Systems, ICACSIS 2016
CountryIndonesia
CityMalang
Period15/10/1616/10/16

Keywords

  • active appearance model
  • facial expression recognition
  • fuzzy c-means
  • human emotion recognition

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