DSpace

King Saud University Repository >
King Saud University >
COLLEGES >
Science Colleges >
College of Computer and Information Sciences >
College of Computer and Information Sciences >

Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/15172

Title: Face Recognition Using Incremental Principal Components Analysis,
Authors: Hatim A
Aboalsamh
Keywords: Incremental principal components analysis (IPCA), candid covariance-free IPCA (CCIPCA), batch PCA, IPCA training and relearning strategies
Issue Date: 2009
Publisher: IEEE Computer Society
Abstract: 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.
URI: http://hdl.handle.net/123456789/15172
Appears in Collections:College of Computer and Information Sciences

Files in This Item:

File Description SizeFormat
Dr.Hassan mathkour-2-conf.docx15.16 kBMicrosoft Word XMLView/Open

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

 

DSpace Software Copyright © 2002-2007 MIT and Hewlett-Packard - Feedback