By Topic

Spontaneous facial expression classification with facial motion vectors

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Sungsoo Park ; Department of Computer Science and Engineering, POSTECH, San 31, Hyoja-Dong, Nam-Gu, Pohang, 790-784, Korea ; Daijin Kim

This paper proposes a novel spontaneous facial expression classification method using the facial motion magnification which transforms the subtle facial expressions into the corresponding exaggerated facial expressions. Facial motion magnification consists of four steps: First, we perform the active appearance model (AAM) fitting to extract 70 facial feature points in the face image sequence. Second, we align the face image sequence using the static three feature points. Third, we estimate the motion vectors of 27 feature points using the feature point tracking method. Finally, we obtain the exaggerated facial expressions by magnifying the motion vectors of the 27 feature points. After facial motion magnification, we recognize the exaggerated facial expressions using the support vector machines (SVM) to classify the facial expression features. Experimental results of the subtle facial expression recognition show promising results of the proposed method.

Published in:

Automatic Face & Gesture Recognition, 2008. FG '08. 8th IEEE International Conference on

Date of Conference:

17-19 Sept. 2008