Genuine Forgery Signature Detection using Radon Transform and K-Nearest Neighbour
Authentication is very much essential in managing security. In modern times, it is one in all priorities. With the advent of technology, dialogue with machines becomes automatic. As a result, the need for authentication for a variety of security purposes is rapidly increasing. For this reason, biome...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Croatian Interdisciplinary Society
2022-12-01
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| Series: | Interdisciplinary Description of Complex Systems |
| Subjects: | |
| Online Access: | https://www.indecs.eu/2022/indecs2022-pp763-774.pdf |
| _version_ | 1797958367310774272 |
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| author | Kiran Bharath, K.N. Gururaj Harinahalli Lokesh Francesco Flammini D.S. Sunil Kumar |
| author_facet | Kiran Bharath, K.N. Gururaj Harinahalli Lokesh Francesco Flammini D.S. Sunil Kumar |
| author_sort | Kiran |
| collection | DOAJ |
| description | Authentication is very much essential in managing security. In modern times, it is one in all priorities. With the advent of technology, dialogue with machines becomes automatic. As a result, the need for authentication for a variety of security purposes is rapidly increasing. For this reason, biometrics-based certification is gaining dramatic momentum. The proposed method describes an off-line Genuine/ Forgery signature classification system using radon transform and K-Nearest Neighbour classifier. Every signature features are extracted by radon transform and they are aligned to get the statistic information of his signature. To align the two signatures, the algorithm used is Extreme Points Warping. Many forged and genuine signatures are selected in K-Nearest Neighbour classifier training. By aligning the test signature with each and every reference signatures of the user, verification of test signature is done. Then the signature can be found whether it is genuine or forgery. A K-Nearest Neighbour is used for classification for the different datasets. The result determines how the proposed procedure is exceeds the current state-of-the-art technology. Approximately, the proposed system's performance is 90 % in signature verification system. |
| first_indexed | 2024-04-11T00:17:52Z |
| format | Article |
| id | doaj.art-bb94b4b16e0841268733849a205a8d3b |
| institution | Directory Open Access Journal |
| issn | 1334-4684 1334-4676 |
| language | English |
| last_indexed | 2024-04-11T00:17:52Z |
| publishDate | 2022-12-01 |
| publisher | Croatian Interdisciplinary Society |
| record_format | Article |
| series | Interdisciplinary Description of Complex Systems |
| spelling | doaj.art-bb94b4b16e0841268733849a205a8d3b2023-01-08T16:43:26ZengCroatian Interdisciplinary SocietyInterdisciplinary Description of Complex Systems1334-46841334-46762022-12-0120676377410.7906/indecs.20.6.7Genuine Forgery Signature Detection using Radon Transform and K-Nearest NeighbourKiran0https://orcid.org/0000-0001-9347-3867Bharath, K.N.1Gururaj Harinahalli Lokesh2https://orcid.org/0000-0002-7529-7449Francesco Flammini3https://orcid.org/0000-0002-2833-7196 D.S. Sunil Kumar4Department of ECE, Vidyavardhaka College of Engineering, Mysuru, IndiaDepartment of ECE, DSATM, Bangalore, IndiaManipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Manipal, IndiaUniversity of Applied Sciences and Arts of Southern Switzerland, Manno, SwitzerlandDepartment of Computer Science, Mangalore University, Mangalore, IndiaAuthentication is very much essential in managing security. In modern times, it is one in all priorities. With the advent of technology, dialogue with machines becomes automatic. As a result, the need for authentication for a variety of security purposes is rapidly increasing. For this reason, biometrics-based certification is gaining dramatic momentum. The proposed method describes an off-line Genuine/ Forgery signature classification system using radon transform and K-Nearest Neighbour classifier. Every signature features are extracted by radon transform and they are aligned to get the statistic information of his signature. To align the two signatures, the algorithm used is Extreme Points Warping. Many forged and genuine signatures are selected in K-Nearest Neighbour classifier training. By aligning the test signature with each and every reference signatures of the user, verification of test signature is done. Then the signature can be found whether it is genuine or forgery. A K-Nearest Neighbour is used for classification for the different datasets. The result determines how the proposed procedure is exceeds the current state-of-the-art technology. Approximately, the proposed system's performance is 90 % in signature verification system.https://www.indecs.eu/2022/indecs2022-pp763-774.pdfsignaturerecognitionk-nearest neighbourradon transform |
| spellingShingle | Kiran Bharath, K.N. Gururaj Harinahalli Lokesh Francesco Flammini D.S. Sunil Kumar Genuine Forgery Signature Detection using Radon Transform and K-Nearest Neighbour Interdisciplinary Description of Complex Systems signature recognition k-nearest neighbour radon transform |
| title | Genuine Forgery Signature Detection using Radon Transform and K-Nearest Neighbour |
| title_full | Genuine Forgery Signature Detection using Radon Transform and K-Nearest Neighbour |
| title_fullStr | Genuine Forgery Signature Detection using Radon Transform and K-Nearest Neighbour |
| title_full_unstemmed | Genuine Forgery Signature Detection using Radon Transform and K-Nearest Neighbour |
| title_short | Genuine Forgery Signature Detection using Radon Transform and K-Nearest Neighbour |
| title_sort | genuine forgery signature detection using radon transform and k nearest neighbour |
| topic | signature recognition k-nearest neighbour radon transform |
| url | https://www.indecs.eu/2022/indecs2022-pp763-774.pdf |
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