On the use of edge features and exponential decaying number of nodes in the hidden layers for handwritten signature recognition
Handwritten signatures are playing an important role in finance, banking and education and more because it is considered the “seal of approval” and remains the most preferred means of authentication. In this paper, an offline handwritten signature authentication algorithm is proposed using the edge...
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| Format: | Article |
| Language: | English English |
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Institute of Advanced Engineering and Science
2018
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| Online Access: | http://irep.iium.edu.my/66217/ http://irep.iium.edu.my/66217/1/66217_On%20the%20Use%20of%20Edge%20Features%20and%20Exponential.pdf http://irep.iium.edu.my/66217/2/66217_On%20the%20Use%20of%20Edge%20Features%20and%20Exponential_SCOPUS.pdf |
| _version_ | 1848786533631393792 |
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| author | Gunawan, Teddy Surya Kartiwi, Mira |
| author_facet | Gunawan, Teddy Surya Kartiwi, Mira |
| author_sort | Gunawan, Teddy Surya |
| building | IIUM Repository |
| collection | Online Access |
| description | Handwritten signatures are playing an important role in finance, banking and education and more because it is considered the “seal of approval” and remains the most preferred means of authentication. In this paper, an offline handwritten signature authentication algorithm is proposed using the edge features and deep feedforward neural network (DFNN). The number of hidden layers in DFNN is configured to be at least one layer and more. In this paper, an exponential decaying number of nodes in the hidden layers was proposed to achieve better recognition rate with reasonable training time. Of the six edge algorithms evaluated, Roberts operator and Canny edge detectors were found to produce better recognition rate. Results showed that the proposed exponential decaying number of nodes in the hidden layers outperform other structure. However, more training data was required so that the proposed DFNN structure could have more efficient learning. |
| first_indexed | 2025-11-14T17:10:33Z |
| format | Article |
| id | iium-66217 |
| institution | International Islamic University Malaysia |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-14T17:10:33Z |
| publishDate | 2018 |
| publisher | Institute of Advanced Engineering and Science |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | iium-662172018-09-12T02:17:58Z http://irep.iium.edu.my/66217/ On the use of edge features and exponential decaying number of nodes in the hidden layers for handwritten signature recognition Gunawan, Teddy Surya Kartiwi, Mira TK7885 Computer engineering Handwritten signatures are playing an important role in finance, banking and education and more because it is considered the “seal of approval” and remains the most preferred means of authentication. In this paper, an offline handwritten signature authentication algorithm is proposed using the edge features and deep feedforward neural network (DFNN). The number of hidden layers in DFNN is configured to be at least one layer and more. In this paper, an exponential decaying number of nodes in the hidden layers was proposed to achieve better recognition rate with reasonable training time. Of the six edge algorithms evaluated, Roberts operator and Canny edge detectors were found to produce better recognition rate. Results showed that the proposed exponential decaying number of nodes in the hidden layers outperform other structure. However, more training data was required so that the proposed DFNN structure could have more efficient learning. Institute of Advanced Engineering and Science 2018-11 Article PeerReviewed application/pdf en http://irep.iium.edu.my/66217/1/66217_On%20the%20Use%20of%20Edge%20Features%20and%20Exponential.pdf application/pdf en http://irep.iium.edu.my/66217/2/66217_On%20the%20Use%20of%20Edge%20Features%20and%20Exponential_SCOPUS.pdf Gunawan, Teddy Surya and Kartiwi, Mira (2018) On the use of edge features and exponential decaying number of nodes in the hidden layers for handwritten signature recognition. Indonesian Journal of Electrical Engineering and Computer Science, 12 (2). pp. 722-728. ISSN 2502-4752 http://www.iaescore.com/journals/index.php/IJEECS/article/view/14544/9403 10.11591/ijeecs.v12.i2.pp722-728 |
| spellingShingle | TK7885 Computer engineering Gunawan, Teddy Surya Kartiwi, Mira On the use of edge features and exponential decaying number of nodes in the hidden layers for handwritten signature recognition |
| title | On the use of edge features and exponential decaying number of nodes in the hidden layers for handwritten signature recognition |
| title_full | On the use of edge features and exponential decaying number of nodes in the hidden layers for handwritten signature recognition |
| title_fullStr | On the use of edge features and exponential decaying number of nodes in the hidden layers for handwritten signature recognition |
| title_full_unstemmed | On the use of edge features and exponential decaying number of nodes in the hidden layers for handwritten signature recognition |
| title_short | On the use of edge features and exponential decaying number of nodes in the hidden layers for handwritten signature recognition |
| title_sort | on the use of edge features and exponential decaying number of nodes in the hidden layers for handwritten signature recognition |
| topic | TK7885 Computer engineering |
| url | http://irep.iium.edu.my/66217/ http://irep.iium.edu.my/66217/ http://irep.iium.edu.my/66217/ http://irep.iium.edu.my/66217/1/66217_On%20the%20Use%20of%20Edge%20Features%20and%20Exponential.pdf http://irep.iium.edu.my/66217/2/66217_On%20the%20Use%20of%20Edge%20Features%20and%20Exponential_SCOPUS.pdf |