Gunawan, Teddy Surya and Mahamud, Norsalha and Kartiwi, Mira (2018) Development of offline handwritten signature authentication using artificial neural network. In: International Conference on Computing, Engineering, and Design (ICCED 2017), 23-25 November 2017, Kuala Lumpur, Malaysia.
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Abstract
Handwritten signatures are playing an important role in finance, banking and education and more because it is considered to be the “seal of approval” and remains the most preferred means of authentication. In this paper, an offline handwritten signature authentication algorithm is proposed using Artificial Neural Network (ANN). As part of the feature extraction, two image filters were used, i.e. Canny edge detector and averaging filter. A feedforward neural network with 1 hidden layer was trained using backpropagation algorithm. The number of nodes in the hidden layer was varied from 80 to 1000. The higher the number of nodes, the higher the recognition rate. Moreover, we found that Canny edge detector is the suitable feature extraction as it produced higher recognition rate compared to the averaging filter.
Item Type: | Conference or Workshop Item (Invited Papers) |
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Additional Information: | 5588/61256 |
Uncontrolled Keywords: | offline handwritten signatures; canny edge detection; averaging filter; artificial neural network. |
Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices |
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button): | Kulliyyah of Engineering > Department of Electrical and Computer Engineering Kulliyyah of Information and Communication Technology > Department of Information System Kulliyyah of Information and Communication Technology > Department of Information System |
Depositing User: | Prof. Dr. Teddy Surya Gunawan |
Date Deposited: | 04 Apr 2018 15:33 |
Last Modified: | 17 Sep 2018 01:28 |
URI: | http://irep.iium.edu.my/id/eprint/61256 |
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