Proceedings of the 2014 International Conference on Global Economy, Commerce and Service Science

Facial Recognition Using Eigenfaces Approach

Authors
Mohd Noah A. Rahman, Armanadurni Abd Rahman, Afzaal H. Seyal, Nursuziana Kamarudin
Corresponding Author
Mohd Noah A. Rahman
Available Online January 2014.
DOI
10.2991/gecss-14.2014.88How to use a DOI?
Keywords
eigenfaces, face recognition, principal component analysis
Abstract

Face recognition has been researched extensively since the early 1950s and it is still an evolving domain for research. However, this application is relatively new in this country as compared to other biometric identifications. This paper seeks to find out the success rate of detection and recognition of the human faces using the eigenfaces method of the principal component analysis. It was conducted using the PCA algorithm on eigenfaces on 30 students using different images stored in a training database. From the experiment conducted, the PCA eigenfaces approach is able to deliver and produce high accuracy results.

Copyright
© 2014, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2014 International Conference on Global Economy, Commerce and Service Science
Series
Advances in Intelligent Systems Research
Publication Date
January 2014
ISBN
978-90-78677-96-3
ISSN
1951-6851
DOI
10.2991/gecss-14.2014.88How to use a DOI?
Copyright
© 2014, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Mohd Noah A. Rahman
AU  - Armanadurni Abd Rahman
AU  - Afzaal H. Seyal
AU  - Nursuziana Kamarudin
PY  - 2014/01
DA  - 2014/01
TI  - Facial Recognition Using Eigenfaces Approach
BT  - Proceedings of the 2014 International Conference on Global Economy, Commerce and Service Science
PB  - Atlantis Press
SP  - 349
EP  - 351
SN  - 1951-6851
UR  - https://doi.org/10.2991/gecss-14.2014.88
DO  - 10.2991/gecss-14.2014.88
ID  - Rahman2014/01
ER  -