Robust facial emotion recognition using a temporal-reinforced approach

Kai-Tai Song*, Chao Yu Lin

*Corresponding author for this work

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

2 Scopus citations

Abstract

In this paper, a temporal-reinforced approach to enhancing emotion recognition from facial images is presented. Shape and texture models of facial images are computed by using active appearance model (AAM), from which facial feature points and geometrical feature values are extracted. The extracted features are used by relevance vector machine (RVM) to recognize emotional states. We propose a temporal analysis approach to recognizing likelihood of emotional categories, such that more subtle emotion, such as degree and ratio of basic emotional states can be obtained. Furthermore, a method is developed to map the recognition result to the arousal-valence plane (A-V Plane). Experimental results verify that the performance of emotion recognition is enhanced by the proposed method.

Original languageEnglish
Title of host publicationInternational Conference on Control, Automation and Systems
PublisherIEEE Computer Society
Pages804-807
Number of pages4
ISBN (Electronic)9788993215069
DOIs
StatePublished - 16 Dec 2014
Event2014 14th International Conference on Control, Automation and Systems, ICCAS 2014 - Gyeonggi-do, Korea, Republic of
Duration: 22 Oct 201425 Oct 2014

Publication series

NameInternational Conference on Control, Automation and Systems
ISSN (Print)1598-7833

Conference

Conference2014 14th International Conference on Control, Automation and Systems, ICCAS 2014
CountryKorea, Republic of
CityGyeonggi-do
Period22/10/1425/10/14

Keywords

  • Facial expression recognition
  • image processing
  • pattern recognition

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