Mix emotion recognition from facial expression using SVM-CRF sequence classifier

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

11 Citations (Scopus)

Abstract

Recently, emotion recognition has gained increasing attention in various applications related to Social Signal Processing (SSP) and human affect. The existing research is mainly focused on six basic emotions (happy, sad, fear, disgust, angry, and surprise). However human expresses many kind of emotions, including mix emotion which has not been explored due to its complexity. We model 12 types of mix emotion recognition from facial expression in a sequence of images using two-stages learning which combines Support Vector Machines (SVM) and Conditional Random Fields (CRF) as sequence classifiers. SVM classifies each image frame and produce emotion label output, subsequently it becomes the input for CRF which yields the mix emotion label of the corresponding observation sequence. We evaluate our proposed model on modified image frames of Cohn Kanade+ dataset, and on our own made mix emotion dataset. We also compare our model with the original CRF model, and our model shows a superior performance result.

Original languageEnglish
Title of host publicationProceedings of 2017 International Conference on Algorithms, Computing and Systems, ICACS 2017
PublisherAssociation for Computing Machinery
Pages27-31
Number of pages5
ISBN (Electronic)9781450352840
DOIs
Publication statusPublished - 10 Aug 2017
Event2017 International Conference on Algorithms, Computing and Systems, ICACS 2017 - Jeju Island, Korea, Republic of
Duration: 10 Aug 201713 Aug 2017

Publication series

NameACM International Conference Proceeding Series
VolumePart F132084

Conference

Conference2017 International Conference on Algorithms, Computing and Systems, ICACS 2017
CountryKorea, Republic of
CityJeju Island
Period10/08/1713/08/17

Keywords

  • Facial expression
  • Mix emotion recognition
  • SVM-CRF classifier
  • Sequence classifier

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