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Offline Signature Verification and Identification using Dimensionality Reduction

by Rashmi .b.n
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 117 - Number 20
Year of Publication: 2015
Authors: Rashmi .b.n
10.5120/20668-3250

Rashmi .b.n . Offline Signature Verification and Identification using Dimensionality Reduction. International Journal of Computer Applications. 117, 20 ( May 2015), 4-6. DOI=10.5120/20668-3250

@article{ 10.5120/20668-3250,
author = { Rashmi .b.n },
title = { Offline Signature Verification and Identification using Dimensionality Reduction },
journal = { International Journal of Computer Applications },
issue_date = { May 2015 },
volume = { 117 },
number = { 20 },
month = { May },
year = { 2015 },
issn = { 0975-8887 },
pages = { 4-6 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume117/number20/20668-3250/ },
doi = { 10.5120/20668-3250 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:59:53.627535+05:30
%A Rashmi .b.n
%T Offline Signature Verification and Identification using Dimensionality Reduction
%J International Journal of Computer Applications
%@ 0975-8887
%V 117
%N 20
%P 4-6
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper we are proposed a novel approach to extracting the features from a hand-written off-line signature. The experiments are carried out on a user created data base. We are extracting the geometrical distance-metric features and pruned projection features. The extracted pruned projection features are huge in dimensions, it's difficult to process and analysis. To reduce the feature matrix dimensions without loss of information, existing stereographic reduction algorithm is used. The patterns are classified using the supervised Knn-classifier. FRR (False Rejection Rate) and FAR (False Acceptance Rate) for Identification by proposed approach is 6% and 7%. And that of Verification is 12. 6% and 13 %.

References
  1. Meenakshi S Arya, Vandana S Inamdar,. "A Preliminary Study on Various Off-line Hand-Written Signature Verification Approaches", ©2010 International Journal of Computer Applications (0975-8887) Volume 1 – No. 9
  2. Edson J. R. Justino, A. El Yacoubi, F. Bortolozzi and R. Sabourin "An Off-Line Signature Verification System Using HMM and Graphometric Features", DAS 2000, 4th IAPR International Workshop on Document Analysis Systems, Riode Janeiro, Brazil, (2000), pp 211--222.
  3. Drouhard, J. P. , R. Sabourin, and M. Godbout, "A neural network approach to off-line signature verification using directional PDF", Pattern Recognition, vol. 29, no. 3,(1996), 415--424.
  4. "Off-line Signature Verification Using HMM for Random, Simple and SkilledForgeries", Edson J. R. Justino 1 , Flávio Bortolozzi 1 , Robert Sabourin 1, 21 PUCPR - Pontifícia Universidade Católica do Paraná ,R. Imaculada Conceição, 1155 CEP:80215-901 - Curitiba - PR – Brazil - {justino, fborto }@ppgia. pucpr. br 2ETS - Ecole de Technologie Supérieure, 1100, rue Notre-Dame Ouest - Montréal (Québec) H3C 1K3 – Canada.
  5. Dubey RB, Sachdeva S. "An Offline Signature Verification Technique". WebmedCentral MISCELLANEOUS 2011:2(5):WMC001919 doi:10. 9754/journal. wmc. 2011. 001919.
  6. Written by Paul Bourke EGG data courtesy of Dr. Per Line.
Index Terms

Computer Science
Information Sciences

Keywords

Pruned projection End-points Distance between two end points Angle made at each end point.

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