By Topic

Fast principal component analysis for face detection using cross-correlation and image decomposition

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
$33 $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)
Hazem M. El-Bakry ; Faculty of Computer Science & Information Systems, Mansoura University, EGYPT ; Mohamed Hamada

In a previous paper, fast PCA implementation for face detection based on cross-correlation in the frequency domain between the input image and eigenvectors was presented. Here, this approach is developed to reduce the computation steps required by fast PCA. The principle of divide and conquer strategy is applied through image decomposition. Each image is divided into small in size sub-images and then each one is tested separately by using a single fast PCA processor. In contrast to using only fast PCA, the speed up ratio is increased with the size of the input image when using fast PCA and image decomposition. Simulation results demonstrate that our proposal is faster than the conventional and fast PCA. Moreover, experimental results for different images show good performance.

Published in:

2009 International Joint Conference on Neural Networks

Date of Conference:

14-19 June 2009