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Face Recognition using Eigenvector and Principle Component Analysis

by Dulal Chakraborty, Sanjit Kumar Saha, Md. Al-amin Bhuiyan
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 50 - Number 10
Year of Publication: 2012
Authors: Dulal Chakraborty, Sanjit Kumar Saha, Md. Al-amin Bhuiyan
10.5120/7811-0947

Dulal Chakraborty, Sanjit Kumar Saha, Md. Al-amin Bhuiyan . Face Recognition using Eigenvector and Principle Component Analysis. International Journal of Computer Applications. 50, 10 ( July 2012), 42-49. DOI=10.5120/7811-0947

@article{ 10.5120/7811-0947,
author = { Dulal Chakraborty, Sanjit Kumar Saha, Md. Al-amin Bhuiyan },
title = { Face Recognition using Eigenvector and Principle Component Analysis },
journal = { International Journal of Computer Applications },
issue_date = { July 2012 },
volume = { 50 },
number = { 10 },
month = { July },
year = { 2012 },
issn = { 0975-8887 },
pages = { 42-49 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume50/number10/7811-0947/ },
doi = { 10.5120/7811-0947 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:49:08.803930+05:30
%A Dulal Chakraborty
%A Sanjit Kumar Saha
%A Md. Al-amin Bhuiyan
%T Face Recognition using Eigenvector and Principle Component Analysis
%J International Journal of Computer Applications
%@ 0975-8887
%V 50
%N 10
%P 42-49
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Face recognition is an important and challenging field in computer vision. This research present a system that is able to recognize a person's face by comparing facial structure to that of a known person which is achieved by using frontal view facing photographs of individuals to render a two-dimensional representation of a human head. Various symmetrization techniques are used for preprocessing the image in order to handle bad illumination and face alignment problem. We used Eigenface approach for face recognition. Eigenfaces are eigenvectors of covariance matrix, representing given image space. Any new face image can then be represented as a linear combination of these Eigenfaces. This makes it easier to match any two given images and thus face recognition process. The implemented eigenface-based technique classified the faces 95% correctly.

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Index Terms

Computer Science
Information Sciences

Keywords

Principle component analysis eigenvector eigenvalue eigenface faces recognition

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