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Offline Signature Verification based on Ensemble of Features using Support Vector Machine

by Sunil Kumar D.S.
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 184 - Number 45
Year of Publication: 2023
Authors: Sunil Kumar D.S.
10.5120/ijca2023922547

Sunil Kumar D.S. . Offline Signature Verification based on Ensemble of Features using Support Vector Machine. International Journal of Computer Applications. 184, 45 ( Feb 2023), 24-29. DOI=10.5120/ijca2023922547

@article{ 10.5120/ijca2023922547,
author = { Sunil Kumar D.S. },
title = { Offline Signature Verification based on Ensemble of Features using Support Vector Machine },
journal = { International Journal of Computer Applications },
issue_date = { Feb 2023 },
volume = { 184 },
number = { 45 },
month = { Feb },
year = { 2023 },
issn = { 0975-8887 },
pages = { 24-29 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume184/number45/32607-2023922547/ },
doi = { 10.5120/ijca2023922547 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:24:56.465486+05:30
%A Sunil Kumar D.S.
%T Offline Signature Verification based on Ensemble of Features using Support Vector Machine
%J International Journal of Computer Applications
%@ 0975-8887
%V 184
%N 45
%P 24-29
%D 2023
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper, we propose a novel feature representation technique namely Edge Histogram and 4 Directional Histogram for offline signature verification system. Edge is a curve or point where the intensity of an image changes rapidly. Edges represent the boundary of object of an image. Edge detection is a process of detecting edges of an image. Several algorithms are available to detect edges effectively from an image. Canny, Roberts, Prewitt and Sobel are several popular available edge detectors. In our approach we used Sobel operator for edge detection. We also applied radon transform on signature samples and obtained fractal properties with the help of box-counting method. Finally fusion of these features forms a feature vector. We employed Support vector machine for classification. Experiments are conducted on bench mark dataset namely CEDAR and GPDS. The obtained experimental results exhibit the performance of the proposed method.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Signature Verification Support Vector Machine Classification Edge Histogram Edge Directional Histogram.

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