An offline signature verification technique using pixels intensity levels

Offline signature recognition has great importance in our day to day activities. Researchers are trying to use them as biometric identification in various areas like banks, security systems and for other identification purposes. Fingerprints, iris, thumb impression and face detection based biometric...

Full description

Bibliographic Details
Main Authors:	Shah, Abdul Salam, Khan, M.N.A., Subhan, Fazli, Fayaz, Muhammad, Shah, Asadullah
Format:	Article
Language:	English
Published:	2016
Subjects:	QA75 Electronic computers. Computer science
Online Access:	http://irep.iium.edu.my/52201/ http://irep.iium.edu.my/52201/ http://irep.iium.edu.my/52201/ http://irep.iium.edu.my/52201/1/an%20offline%20signature%20verification%20technique%20using%20pixels%20intensity%20levels.pdf

id	iium-52201
recordtype	eprints
spelling	iium-522012017-01-09T06:38:05Z http://irep.iium.edu.my/52201/ An offline signature verification technique using pixels intensity levels Shah, Abdul Salam Khan, M.N.A. Subhan, Fazli Fayaz, Muhammad Shah, Asadullah QA75 Electronic computers. Computer science Offline signature recognition has great importance in our day to day activities. Researchers are trying to use them as biometric identification in various areas like banks, security systems and for other identification purposes. Fingerprints, iris, thumb impression and face detection based biometrics are successfully used for identification of individuals because of their static nature. However, people’s signatures show variability that makes it difficult to recognize the original signatures correctly and to use them as biometrics. The handwritten signatures have importance in banks for cheque, credit card processing, legal and financial transactions, and the signatures are the main target of fraudulence. To deal with complex signatures, there should be a robust signature verification method in places such as banks that can correctly classify the signatures into genuine or forgery to avoid financial frauds. This paper, presents a pixels intensity level based offline signature verification model for the correct classification of signatures. To achieve the target, three statistical classifiers; Decision Tree (J48), probability based Naïve Bayes (NB tree) and Euclidean distance based k-Nearest Neighbor (IBk), are used. For comparison of the accuracy rates of offline signatures with online signatures, three classifiers were applied on online signature database and achieved a 99.90% accuracy rate with decision tree (J48), 99.82% with Naïve Bayes Tree and 98.11% with K-Nearest Neighbor (with 10 fold cross validation). The results of offline signatures were 64.97% accuracy rate with decision tree (J48), 76.16% with Naïve Bayes Tree and 91.91% with k-Nearest Neighbor (IBk) (without forgeries). The accuracy rate dropped with the inclusion of forgery signatures as, 55.63% accuracy rate with decision tree (J48), 67.02% with Naïve Bayes Tree and 88.12% (with forgeries). 2016 Article PeerReviewed application/pdf en http://irep.iium.edu.my/52201/1/an%20offline%20signature%20verification%20technique%20using%20pixels%20intensity%20levels.pdf Shah, Abdul Salam and Khan, M.N.A. and Subhan, Fazli and Fayaz, Muhammad and Shah, Asadullah (2016) An offline signature verification technique using pixels intensity levels. International Journal of Signal Processing, Image Processing and Pattern Recognition, 9 (8). pp. 205-222. ISSN 2005-4254 http://www.sersc.org/journals/IJSIP/vol9_no8/18.pdf http://dx.doi.org/10.14257/ijsip.2016.9.8.18
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English
topic	QA75 Electronic computers. Computer science
spellingShingle	QA75 Electronic computers. Computer science Shah, Abdul Salam Khan, M.N.A. Subhan, Fazli Fayaz, Muhammad Shah, Asadullah An offline signature verification technique using pixels intensity levels
description	Offline signature recognition has great importance in our day to day activities. Researchers are trying to use them as biometric identification in various areas like banks, security systems and for other identification purposes. Fingerprints, iris, thumb impression and face detection based biometrics are successfully used for identification of individuals because of their static nature. However, people’s signatures show variability that makes it difficult to recognize the original signatures correctly and to use them as biometrics. The handwritten signatures have importance in banks for cheque, credit card processing, legal and financial transactions, and the signatures are the main target of fraudulence. To deal with complex signatures, there should be a robust signature verification method in places such as banks that can correctly classify the signatures into genuine or forgery to avoid financial frauds. This paper, presents a pixels intensity level based offline signature verification model for the correct classification of signatures. To achieve the target, three statistical classifiers; Decision Tree (J48), probability based Naïve Bayes (NB tree) and Euclidean distance based k-Nearest Neighbor (IBk), are used. For comparison of the accuracy rates of offline signatures with online signatures, three classifiers were applied on online signature database and achieved a 99.90% accuracy rate with decision tree (J48), 99.82% with Naïve Bayes Tree and 98.11% with K-Nearest Neighbor (with 10 fold cross validation). The results of offline signatures were 64.97% accuracy rate with decision tree (J48), 76.16% with Naïve Bayes Tree and 91.91% with k-Nearest Neighbor (IBk) (without forgeries). The accuracy rate dropped with the inclusion of forgery signatures as, 55.63% accuracy rate with decision tree (J48), 67.02% with Naïve Bayes Tree and 88.12% (with forgeries).
format	Article
author	Shah, Abdul Salam Khan, M.N.A. Subhan, Fazli Fayaz, Muhammad Shah, Asadullah
author_facet	Shah, Abdul Salam Khan, M.N.A. Subhan, Fazli Fayaz, Muhammad Shah, Asadullah
author_sort	Shah, Abdul Salam
title	An offline signature verification technique using pixels intensity levels
title_short	An offline signature verification technique using pixels intensity levels
title_full	An offline signature verification technique using pixels intensity levels
title_fullStr	An offline signature verification technique using pixels intensity levels
title_full_unstemmed	An offline signature verification technique using pixels intensity levels
title_sort	offline signature verification technique using pixels intensity levels
publishDate	2016
url	http://irep.iium.edu.my/52201/ http://irep.iium.edu.my/52201/ http://irep.iium.edu.my/52201/ http://irep.iium.edu.my/52201/1/an%20offline%20signature%20verification%20technique%20using%20pixels%20intensity%20levels.pdf
first_indexed	2023-09-18T21:14:00Z
last_indexed	2023-09-18T21:14:00Z
_version_	1777411440245735424

An offline signature verification technique using pixels intensity levels

Similar Items