TY - GEN
T1 - Seeing through the expression
T2 - 2013 IEEE International Conference on Multimedia and Expo, ICME 2013
AU - Hsu, Lun Kai
AU - Tseng, Wen Sheng
AU - Kang, Li Wei
AU - Wang, Yu Chiang Frank
PY - 2013
Y1 - 2013
N2 - 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.
AB - 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.
KW - Expression recognition
KW - emotion recognition
KW - subspace learning
UR - http://www.scopus.com/inward/record.url?scp=84885606764&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84885606764&partnerID=8YFLogxK
U2 - 10.1109/ICME.2013.6607638
DO - 10.1109/ICME.2013.6607638
M3 - Conference contribution
AN - SCOPUS:84885606764
SN - 9781479900152
T3 - Proceedings - IEEE International Conference on Multimedia and Expo
BT - 2013 IEEE International Conference on Multimedia and Expo, ICME 2013
Y2 - 15 July 2013 through 19 July 2013
ER -