Seeing through the expression: Bridging the gap between expression and emotion recognition

Lun Kai Hsu, Wen Sheng Tseng, Li Wei Kang, Yu Chiang Frank Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

In this paper, we propose a novel approach for visualizing and recognizing different emotion categories using facial expression images. Extended by the unsupervised nonlinear dimension reduction technique of locally linear embedding (LLE), we propose a supervised LLE (sLLE) algorithm utilizing emotion labels of face expression images. While existing works typically aim at training on such labeled data for emotion recognition, our approach allows one to derive subspaces for visualizing facial expression images within and across different emotion categories, and thus emotion recognition can be properly performed. In our work, we relate the resulting two-dimensional subspace to the valence-arousal emotion space, in which our method is observed to automatically identify and discriminate emotions in different degrees. Experimental results on two facial emotion datasets verify the effectiveness of our algorithm. With reduced numbers of feature dimensions (2D or beyond), our approach is shown to achieve promising emotion recognition performance.

Original languageEnglish
Title of host publication2013 IEEE International Conference on Multimedia and Expo, ICME 2013
DOIs
Publication statusPublished - 2013
Externally publishedYes
Event2013 IEEE International Conference on Multimedia and Expo, ICME 2013 - San Jose, CA, United States
Duration: 2013 Jul 152013 Jul 19

Publication series

NameProceedings - IEEE International Conference on Multimedia and Expo
ISSN (Print)1945-7871
ISSN (Electronic)1945-788X

Other

Other2013 IEEE International Conference on Multimedia and Expo, ICME 2013
Country/TerritoryUnited States
CitySan Jose, CA
Period2013/07/152013/07/19

Keywords

  • emotion recognition
  • Expression recognition
  • subspace learning

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications

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