Online handwritten signature verification using neural network classifier based on principal 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...
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Hindawi Limited
2023
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| author | Iranmanesh V. Ahmad S.M.S. Adnan W.A.W. Yussof S. Arigbabu O.A. Malallah F.L. |
| author2 | 56047920000 |
| author_facet | 56047920000 Iranmanesh V. Ahmad S.M.S. Adnan W.A.W. Yussof S. Arigbabu O.A. Malallah F.L. |
| author_sort | Iranmanesh V. |
| building | UNITEN Library |
| collection | Institutional Repository |
| content_provider | Universiti Tenaga Nasional |
| content_source | UNITEN Institutional Repository |
| continent | Asia |
| country | Malaysia |
| 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%. © 2014 Vahab Iranmanesh et al. |
| format | Article |
| id | my.uniten.dspace-21959 |
| institution | Universiti Tenaga Nasional |
| publishDate | 2023 |
| publisher | Hindawi Limited |
| record_format | dspace |
| spelling | my.uniten.dspace-219592023-05-16T10:46:18Z Online handwritten signature verification using neural network classifier based on principal component analysis Iranmanesh V. Ahmad S.M.S. Adnan W.A.W. Yussof S. Arigbabu O.A. Malallah F.L. 56047920000 24721182400 6506665562 16023225600 56047591000 56102103900 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%. © 2014 Vahab Iranmanesh et al. Final 2023-05-16T02:46:17Z 2023-05-16T02:46:17Z 2014 Article 10.1155/2014/381469 2-s2.0-84964238181 https://www.scopus.com/inward/record.uri?eid=2-s2.0-84964238181&doi=10.1155%2f2014%2f381469&partnerID=40&md5=ee5a30e9295f33730a9ba41bca7a59b1 https://irepository.uniten.edu.my/handle/123456789/21959 2014 381469 All Open Access, Gold, Green Hindawi Limited Scopus |
| spellingShingle | Iranmanesh V. Ahmad S.M.S. Adnan W.A.W. Yussof S. Arigbabu O.A. Malallah F.L. Online handwritten signature verification using neural network classifier based on principal component analysis |
| title | Online handwritten signature verification using neural network classifier based on principal component analysis |
| title_full | Online handwritten signature verification using neural network classifier based on principal component analysis |
| title_fullStr | Online handwritten signature verification using neural network classifier based on principal component analysis |
| title_full_unstemmed | Online handwritten signature verification using neural network classifier based on principal component analysis |
| title_short | Online handwritten signature verification using neural network classifier based on principal component analysis |
| title_sort | online handwritten signature verification using neural network classifier based on principal component analysis |
| url_provider | http://dspace.uniten.edu.my/ |