NDLI: Face Recognition Using Incremental Principal Components Analysis

Content Provider	IEEE Xplore Digital Library
Author	Aboalsamh, H.A. Mathkour, H.I. Assassa, G.M.R. Mursi, M.F.M.
Copyright Year	2009
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.
Starting Page	39
Ending Page	43
File Size	327041
Page Count	5
File Format	PDF
ISBN	9780769535388
DOI	10.1109/ICC.2009.56
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-04-02
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Face recognition Humans IPCA training and relearning strategies Independent component analysis Educational institutions Batch PCA Information technology Computer science Incremental principal components analysis (IPCA) Candid covariance-free IPCA (CCIPCA) Linear discriminant analysis Computer security Principal component analysis Testing
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Evolutionary Eigenspace Learning using CCIPCA and IPCA for Face Recognition

ing Candid Covariance-Free Incremental Principal Components Analysis (CCIPCA)

Evolutionary Eigenspace Learning using CCIPCA and IPCA for Face Recognition

Incremental PCA-LDA algorithm

Candid Covariance-free Incremental Principal Component Analysis (2003)

Face recognition: using principal components and independent components

A novel incremental principal component analysis and its application for face recognition (2006)

Sparse eigenfaces analysis for recognition

Incremental PCA-LDA Algorithm

Face Recognition Using Incremental Principal Components Analysis

Similar Documents

Evolutionary Eigenspace Learning using CCIPCA and IPCA for Face Recognition

ing Candid Covariance-Free Incremental Principal Components Analysis (CCIPCA)

Evolutionary Eigenspace Learning using CCIPCA and IPCA for Face Recognition

Incremental PCA-LDA algorithm

Candid Covariance-free Incremental Principal Component Analysis (2003)

Face recognition: using principal components and independent components

A novel incremental principal component analysis and its application for face recognition (2006)

Sparse eigenfaces analysis for recognition

Incremental PCA-LDA Algorithm

Face Recognition Using Incremental Principal Components Analysis