By Topic

Random Projection with Robust Linear Discriminant Analysis Model in Face 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 $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)
Pang Ying Han ; Fac. of Inf. Sci. & Technol., Multimedia Univ., Cyberjaya ; Jin, A.T.B.

This paper presents a face recognition technique with two techniques: random projection (RP) and robust linear discriminant analysis model (RDM). RDM is an enhanced version of fisher's linear discriminant with energy-adaptive regularization criteria. It is able to yield better discrimination performance. Same as Fisher's Linear Discriminant, it also faces the singularity problem of within-class scatter. Thus, a dimensionality reduction technique, such as principal component analysis (PCA), is needed to deal with this problem. In this paper, RP is used as an alternative to PCA in RDM in the application of face recognition. Unlike PCA, RP is training data independent and the random subspace computation is relatively simple. The experimental results illustrate that the proposed algorithm is able to attain better recognition performance (error rate is approximately 5% lower) compared to Fisherfaces.

Published in:

Computer Graphics, Imaging and Visualisation, 2007. CGIV '07

Date of Conference:

14-17 Aug. 2007