By Topic

Local variance projection log energy entropy features for illumination robust 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)
Jia-shu Zhang ; Sch. of Inf. Sci. & Technol., Southwest Jiaotong Univ., Chengdu ; Cun-Jian Chen

This paper presents local variance projection log energy entropy (LVP-LEE) features for illumination robust face recognition. In the proposed method, one face image is firstly divided into non-overlapping small blocks, and then the vertical and horizontal variance projection distributions of each small face block are computed, using to extract features in terms of log energy entropy concept. Finally, features from individual block are constructed as the whole feature vector of face image for face recognition. The proposed LVP-LEE features have been evaluated based on the CMU PIE face database and compared with PCA, modular PCA and sub pattern PCA features. Experimental results show that the proposed LVP-LEE features are more effective for illumination robust face recognition.

Published in:

Biometrics and Security Technologies, 2008. ISBAST 2008. International Symposium on

Date of Conference:

23-24 April 2008