By Topic

A hierarchical algorithm with multi-feature fusion for facial expression recognition

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

3 Author(s)
Zheng Zhang ; State Key Laboratory of Intelligent Technology and Systems, Department of Electronic Engineering, Tsinghua University, Beijing 100084, P.R. China ; Chi Fang ; Xiaoqing Ding

In this paper, a novel hierarchical algorithm with multi-feature fusion is proposed for facial expression recognition. In this area, many people have proposed many good results, but few of them made good use of the distribution characteristic of facial expression itself. In the analysis of the feature distribution, we find happiness and surprise are clearly separated from the other expressions. So we aim to distinguish these two expressions in the first layer of our algorithm using Gabor features. In the second layer, we use Gabor and LBP features respectively to classify the other five expressions. And a well designed result fusion of two branches is adopted to improve the accuracy. Experiments results on the Cohn-Kanade database show that our algorithm achieves excellent accuracy. Furthermore, our algorithm also performs well in our hybrid database, in which there are extensive variations of expressions. It demonstrates the good generalization ability of our algorithm.

Published in:

Pattern Recognition (ICPR), 2012 21st International Conference on

Date of Conference:

11-15 Nov. 2012