An Efficient Automated Attendance Entering System by Eliminating Counterfeit Signatures using Kolmogorov Smirnov Test

An Efficient Automated Attendance Entering System by Eliminating Counterfeit Signatures using Kolmogorov Smirnov Test

Lokesha Weerasinghe

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B.H. Sudantha

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Sri Lanka Institute of Information Technology

An Efficient Automated Attendance Entering System by Eliminating Counterfeit Signatures using Kolmogorov Smirnov Test

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Abstract

Maintaining the attendance database of thousands of students has become a tedious task in the universities in Sri Lanka. This paper comprises of 3 phases: signature extraction, signature recognition, and signature verification to automate the process. We applied necessary image processing techniques, and extracted useful features from each signature. Support Vector Machine (SVM), multiclass Support Vector Machine and Kolmogorov Smirnov test is used to signature classification, recognition, and verification respectively. The described method in this report represents an effective and accurate approach to automatic signature recognition and verification. It is capable of matching, classifying, and verifying the test signatures with the database of 83.33%, 100%, and 100% accuracy respectively.

References

20 Cites in Article

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Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

Lokesha Weerasinghe. 2019. \u201cAn Efficient Automated Attendance Entering System by Eliminating Counterfeit Signatures using Kolmogorov Smirnov Test\u201d. Global Journal of Computer Science and Technology - G: Interdisciplinary GJCST-G Volume 19 (GJCST Volume 19 Issue G2).

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GJCST Volume 19 Issue G2

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Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords

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Classification

GJCST-G Classification K.3.0

Submission ReceivedDecember 7, 2018
Peer Review Double Blind
Handling Editor
Accepted December 31, 2018
Published January 15, 2019

Version of record

v1.2

Issue date

May 27, 2019

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Article Matrices

Total Score: 102

Country: Sri Lanka

Subject: Global Journal of Computer Science and Technology - G: Interdisciplinary

Authors: Lokesha Weerasinghe, B.H. Sudantha (PhD/Dr. count: 0)

View Count (all-time): 320

Total Views (Real + Logic): 5306

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Publish Date: 2019 05, Mon

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Total Views: 5306

Total Downloads: 1398

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