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...

Full description

Bibliographic Details
Main Authors:	Gunawan, Teddy Surya, Kartiwi, Mira
Format:	Article
Language:	English English
Published:	Institute of Advanced Engineering and Science 2018
Subjects:	TK7885 Computer engineering
Online Access:	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

id	iium-66217
recordtype	eprints
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
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English English
topic	TK7885 Computer engineering
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
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.
format	Article
author	Gunawan, Teddy Surya Kartiwi, Mira
author_facet	Gunawan, Teddy Surya Kartiwi, Mira
author_sort	Gunawan, Teddy Surya
title	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_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_sort	on the use of edge features and exponential decaying number of nodes in the hidden layers for handwritten signature recognition
publisher	Institute of Advanced Engineering and Science
publishDate	2018
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
first_indexed	2023-09-18T21:33:58Z
last_indexed	2023-09-18T21:33:58Z
_version_	1777412697052151808

On the use of edge features and exponential decaying number of nodes in the hidden layers for handwritten signature recognition

Similar Items