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Reseach Article

Face Recognition using Principle Component Analysis

Published on April 2012 by Sarala A. Dabhade, Mrunal S. Bewoor
Emerging Trends in Computer Science and Information Technology (ETCSIT2012)
Foundation of Computer Science USA
ETCSIT - Number 3
April 2012
Authors: Sarala A. Dabhade, Mrunal S. Bewoor
c373e265-4ec3-409e-86c8-eebd73eefc28

Sarala A. Dabhade, Mrunal S. Bewoor . Face Recognition using Principle Component Analysis. Emerging Trends in Computer Science and Information Technology (ETCSIT2012). ETCSIT, 3 (April 2012), 7-10.

@article{
author = { Sarala A. Dabhade, Mrunal S. Bewoor },
title = { Face Recognition using Principle Component Analysis },
journal = { Emerging Trends in Computer Science and Information Technology (ETCSIT2012) },
issue_date = { April 2012 },
volume = { ETCSIT },
number = { 3 },
month = { April },
year = { 2012 },
issn = 0975-8887,
pages = { 7-10 },
numpages = 4,
url = { /proceedings/etcsit/number3/5975-1018/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 Emerging Trends in Computer Science and Information Technology (ETCSIT2012)
%A Sarala A. Dabhade
%A Mrunal S. Bewoor
%T Face Recognition using Principle Component Analysis
%J Emerging Trends in Computer Science and Information Technology (ETCSIT2012)
%@ 0975-8887
%V ETCSIT
%N 3
%P 7-10
%D 2012
%I International Journal of Computer Applications
Abstract

The objective of this paper is to develop the Image processing and Face Recognition using Principle Component Analysis and Log-Gabor filter . Face is a complex multidimensional visual model and developing a computational model for face recognition is difficult. Eigenfaces approach is a principal component analysis method, in which a small set of characteristic pictures are used to describe the variation between face images. This paper gives the simple implementation of face recognition using principal component analysis, based on information theory concepts, seek a computational model that best describes a face, by extracting the most relevant information contained in that face. The main advantages of the proposed method are its simple implementation, training, and very high recognition accuracy. we implementing the system to find the locations of Log-Gabor features with maximal magnitudes at single scale and multiple orientations using sliding window -based search and then use the same feature locations for all other scales. For further feature compression we used Principal Component Analysis (PCA) because its simple implementation, fast training and because using PCA with Euclidean -based distance measure it is possible to achieve similar recognition accuracy like using EBGM and LDA –based recognition methods

References
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Index Terms

Computer Science
Information Sciences

Keywords

Pca log-gabor Filter sliding Window Based Search euclidean Based Distance Measure