Online handwritten signature verification using neural network classifier based on principle component analysis
One of the main difficulties in designing online signature verification (OSV) system is to find the most distinctive features with high discriminating capabilities for the verification, particularly, with regard to the high variability which is inherent in genuine handwritten signatures, coupled wit...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Hindawi Publishing Corporation
2014
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| Online Access: | http://psasir.upm.edu.my/id/eprint/34746/ http://psasir.upm.edu.my/id/eprint/34746/1/Online%20Handwritten%20Signature%20Verification%20Using%20Neural.pdf |
| _version_ | 1848847860041252864 |
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| author | Iranmanesh, Vahab Syed Ahmad Abdul Rahman, Sharifah Mumtazah Wan Adnan, Wan Azizun Yussof, Salman Arigbabu, Olasimbo Ayodeji Malallah, Fahad Layth |
| author_facet | Iranmanesh, Vahab Syed Ahmad Abdul Rahman, Sharifah Mumtazah Wan Adnan, Wan Azizun Yussof, Salman Arigbabu, Olasimbo Ayodeji Malallah, Fahad Layth |
| author_sort | Iranmanesh, Vahab |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | One of the main difficulties in designing online signature verification (OSV) system is to find the most distinctive features with high discriminating capabilities for the verification, particularly, with regard to the high variability which is inherent in genuine handwritten signatures, coupled with the possibility of skilled forgeries having close resemblance to the original counterparts. In this paper, we proposed a systematic approach to online signature verification through the use of multilayer perceptron (MLP) on a subset of principal component analysis (PCA) features. The proposed approach illustrates a feature selection technique on the usually discarded information from PCA computation, which can be significant in attaining reduced error rates. The experiment is performed using 4000 signature samples from SIGMA database, which yielded a false acceptance rate (FAR) of 7.4% and a false rejection rate (FRR) of 6.4%. |
| first_indexed | 2025-11-15T09:25:19Z |
| format | Article |
| id | upm-34746 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T09:25:19Z |
| publishDate | 2014 |
| publisher | Hindawi Publishing Corporation |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-347462015-12-21T14:06:24Z http://psasir.upm.edu.my/id/eprint/34746/ Online handwritten signature verification using neural network classifier based on principle component analysis Iranmanesh, Vahab Syed Ahmad Abdul Rahman, Sharifah Mumtazah Wan Adnan, Wan Azizun Yussof, Salman Arigbabu, Olasimbo Ayodeji Malallah, Fahad Layth One of the main difficulties in designing online signature verification (OSV) system is to find the most distinctive features with high discriminating capabilities for the verification, particularly, with regard to the high variability which is inherent in genuine handwritten signatures, coupled with the possibility of skilled forgeries having close resemblance to the original counterparts. In this paper, we proposed a systematic approach to online signature verification through the use of multilayer perceptron (MLP) on a subset of principal component analysis (PCA) features. The proposed approach illustrates a feature selection technique on the usually discarded information from PCA computation, which can be significant in attaining reduced error rates. The experiment is performed using 4000 signature samples from SIGMA database, which yielded a false acceptance rate (FAR) of 7.4% and a false rejection rate (FRR) of 6.4%. Hindawi Publishing Corporation 2014 Article PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/34746/1/Online%20Handwritten%20Signature%20Verification%20Using%20Neural.pdf Iranmanesh, Vahab and Syed Ahmad Abdul Rahman, Sharifah Mumtazah and Wan Adnan, Wan Azizun and Yussof, Salman and Arigbabu, Olasimbo Ayodeji and Malallah, Fahad Layth (2014) Online handwritten signature verification using neural network classifier based on principle component analysis. The Scientific World Journal, 2014. art. no. 381469. pp. 1-8. ISSN 2356-6140; ESSN: 1537-744X http://www.hindawi.com/journals/tswj/2014/381469/abs/ 10.1155/2014/381469 |
| spellingShingle | Iranmanesh, Vahab Syed Ahmad Abdul Rahman, Sharifah Mumtazah Wan Adnan, Wan Azizun Yussof, Salman Arigbabu, Olasimbo Ayodeji Malallah, Fahad Layth Online handwritten signature verification using neural network classifier based on principle component analysis |
| title | Online handwritten signature verification using neural network classifier based on principle component analysis |
| title_full | Online handwritten signature verification using neural network classifier based on principle component analysis |
| title_fullStr | Online handwritten signature verification using neural network classifier based on principle component analysis |
| title_full_unstemmed | Online handwritten signature verification using neural network classifier based on principle component analysis |
| title_short | Online handwritten signature verification using neural network classifier based on principle component analysis |
| title_sort | online handwritten signature verification using neural network classifier based on principle component analysis |
| url | http://psasir.upm.edu.my/id/eprint/34746/ http://psasir.upm.edu.my/id/eprint/34746/ http://psasir.upm.edu.my/id/eprint/34746/ http://psasir.upm.edu.my/id/eprint/34746/1/Online%20Handwritten%20Signature%20Verification%20Using%20Neural.pdf |