By Topic

Contourlet-Based Feature Extraction with PCA for 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)
Boukabou, W.R. ; Inst. of Electron., Commun. & Inf. Technol., Queen''s Univ. Belfast, Belfast ; Bouridane, A.

Face recognition is still a challenging task because face images can vary considerably in terms of facial expressions, lighting conditions, ... etc. It is commonly known that the use of multiresolution filter banks improve the recognition accuracy of image based biometric systems. In this paper, we propose to investigate the usefulness of the multiscale and directionality properties of the contourlet transform with a view to extract more discriminant features in order to further enhance the performance of the well known principal component analysis method when applied to face recognition. The proposed method has been extensively assessed using two different databases: the YALE Face Database and the FERET Database. A series of experiments have been carried out and a comparative study suggests the efficiency of the Contourlet Transform in enhancing the classification rates of a number of known face recognition algorithms.

Published in:

Adaptive Hardware and Systems, 2008. AHS '08. NASA/ESA Conference on

Date of Conference:

22-25 June 2008