Cart (Loading....) | Create Account
Close category search window

Adaptive discriminative metric learning 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
$31 $31
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)
Yan, H. ; Dept. of Mech. Eng., Nat. Univ. of Singapore, Singapore, Singapore ; Ang, M.H. ; Poo, A.N.

The authors propose in this study a new adaptive discriminative metric learning method for facial expression recognition. Although a number of methods have been proposed for facial expression recognition, most of them apply the conventional Euclidean distance metric to measure the similarity/dissimilarity of face expression images and cannot effectively characterise such similarity/dissimilarity of these images because the intrinsic space of face images usually do not lie in such an Euclidean space. Motivated by the fact that between-class facial images with small differences are more easily mis-classified than those with large differences, the authors propose learning an adaptive metric by imposing large penalties on between-class samples with small differences and small penalties on those samples with large differences simultaneously, such that more discriminative information can be extracted in the learned distance metric for facial expression recognition. Experimental results on three widely used face datasets are presented to demonstrate the efficacy of the proposed method.

Published in:

Biometrics, IET  (Volume:1 ,  Issue: 3 )

Date of Publication:

Sept. 2012

Need Help?

IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.