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

Face Recognition Using Incremental Principal Components Analysis

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

4 Author(s)
Aboalsamh, H.A. ; Center of Excellence in Inf. Assurance (CoEIA), King Saud Univ. Riyadh, Riyadh, Saudi Arabia ; Mathkour, H.I. ; Assassa, G.M.R. ; Mursi, M.F.M.

Human face recognition plays a significant role in security applications for access control and real time video surveillance systems, and robotics. Popular approaches for face recognition, such as principal components analysis (PCA), rely on static datasets where training is carried in a batch-mode on a pre-available image set. Real world applications require that the training set be dynamic of evolving nature where within the framework of continuous learning new training images are continuously added to the original set; this would trigger a costly frequent re-computation of the eigen space representation via repeating an entire batch-based training that includes the new images. Incremental PCA methods allow adding new images and updating the PCA representation, and offer the advantage of dispensing with the recently added images after model update. In this paper, various incremental PCA (IPCA) training and relearning strategies are proposed and applied to the candid covariance-free incremental principal component algorithm. The effect of the number of increments and size of the eigen vectors on the correct rate of recognition are studied. The results suggest that batch PCA is inferior to the four considered IPCA1-4, and that all IPCAs are practically equivalent with IPCA3 yielding slightly better results than the other IPCAs.

Published in:

Computing, Engineering and Information, 2009. ICC '09. International Conference on

Date of Conference:

2-4 April 2009

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.