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Reseach Article

Face Recognition using SIFT by varying Distance Calculation Matching Method

by Hirdesh Kumar, Padmavati
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 47 - Number 3
Year of Publication: 2012
Authors: Hirdesh Kumar, Padmavati
10.5120/7168-9739

Hirdesh Kumar, Padmavati . Face Recognition using SIFT by varying Distance Calculation Matching Method. International Journal of Computer Applications. 47, 3 ( June 2012), 20-26. DOI=10.5120/7168-9739

@article{ 10.5120/7168-9739,
author = { Hirdesh Kumar, Padmavati },
title = { Face Recognition using SIFT by varying Distance Calculation Matching Method },
journal = { International Journal of Computer Applications },
issue_date = { June 2012 },
volume = { 47 },
number = { 3 },
month = { June },
year = { 2012 },
issn = { 0975-8887 },
pages = { 20-26 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume47/number3/7168-9739/ },
doi = { 10.5120/7168-9739 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:40:55.886182+05:30
%A Hirdesh Kumar
%A Padmavati
%T Face Recognition using SIFT by varying Distance Calculation Matching Method
%J International Journal of Computer Applications
%@ 0975-8887
%V 47
%N 3
%P 20-26
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Scale Invariant Feature Transform (SIFT) is a method for extracting distinctive invariant feature from images [1]. SIFT has been applied to many problems such as face recognition and object recognition [18], [19], [20], [21]. We have analyzed performance of SIFT using Euclidean distance as a matching algorithm. Further the matching rate can be enhanced/improved by changing distance calculation methods used for matching between two face images. So this paper also describes face recognition under various distance calculation methods like Correlation and Cosine. The experiments are conducted on different images of ORL face database [17] and Indian Face database [16] by changing illumination condition, scaling and rotation. From the experiments, it is shown that cosine and correlation distance calculation methods have performed well compared to the Euclidean distance matching method of original SIFT.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Face Recognition Scale Invariant Feature Transform Sift