Features selection for offline handwritten signature verification: state of the art
This research comes out with an in-depth review of widely used techniques to handwritten signature verification based, feature selection techniques. The focus of this research is to explore best features selection criteria for signature verification to avoid forgery. This paper further present pros...
Saved in:
Main Authors: | , , , , |
---|---|
Format: | Article |
Published: |
Inderscience Publishers
2018
|
Subjects: | |
Online Access: | http://eprints.utm.my/id/eprint/84622/ http://dx.doi.org/10.1504/IJCVR.2018.095590 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
id |
my.utm.84622 |
---|---|
record_format |
eprints |
spelling |
my.utm.846222020-02-27T03:21:17Z http://eprints.utm.my/id/eprint/84622/ Features selection for offline handwritten signature verification: state of the art Ebrahim, Anwar Yahya Kolivand, Hoshang Rehman, Amjad Mohd. Rahim, Mohd. Shafry Saba, Tanzila QA75 Electronic computers. Computer science TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering This research comes out with an in-depth review of widely used techniques to handwritten signature verification based, feature selection techniques. The focus of this research is to explore best features selection criteria for signature verification to avoid forgery. This paper further present pros and cons of local and global features selection techniques, reported in the state of art. Experiments are conducted on benchmark databases for signature verification systems (GPDS). Results are tested using two standard protocols; GPDS and the program for rate estimation and feature selection. The current precision of the signature verification techniques reported in state of art are compared on benchmark database and possible solutions are suggested to improve the accuracy. As the equal error rate is an important factor for evaluating the signature verification's accuracy, the results show that the feature selection methods have successfully contributed toward efficient signature verification. Inderscience Publishers 2018 Article PeerReviewed Ebrahim, Anwar Yahya and Kolivand, Hoshang and Rehman, Amjad and Mohd. Rahim, Mohd. Shafry and Saba, Tanzila (2018) Features selection for offline handwritten signature verification: state of the art. International Journal of Computational Vision and Robotics, 8 (6). pp. 606-622. ISSN 1752-9131 http://dx.doi.org/10.1504/IJCVR.2018.095590 DOI:10.1504/IJCVR.2018.095590 |
institution |
Universiti Teknologi Malaysia |
building |
UTM Library |
collection |
Institutional Repository |
continent |
Asia |
country |
Malaysia |
content_provider |
Universiti Teknologi Malaysia |
content_source |
UTM Institutional Repository |
url_provider |
http://eprints.utm.my/ |
topic |
QA75 Electronic computers. Computer science TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering |
spellingShingle |
QA75 Electronic computers. Computer science TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering Ebrahim, Anwar Yahya Kolivand, Hoshang Rehman, Amjad Mohd. Rahim, Mohd. Shafry Saba, Tanzila Features selection for offline handwritten signature verification: state of the art |
description |
This research comes out with an in-depth review of widely used techniques to handwritten signature verification based, feature selection techniques. The focus of this research is to explore best features selection criteria for signature verification to avoid forgery. This paper further present pros and cons of local and global features selection techniques, reported in the state of art. Experiments are conducted on benchmark databases for signature verification systems (GPDS). Results are tested using two standard protocols; GPDS and the program for rate estimation and feature selection. The current precision of the signature verification techniques reported in state of art are compared on benchmark database and possible solutions are suggested to improve the accuracy. As the equal error rate is an important factor for evaluating the signature verification's accuracy, the results show that the feature selection methods have successfully contributed toward efficient signature verification. |
format |
Article |
author |
Ebrahim, Anwar Yahya Kolivand, Hoshang Rehman, Amjad Mohd. Rahim, Mohd. Shafry Saba, Tanzila |
author_facet |
Ebrahim, Anwar Yahya Kolivand, Hoshang Rehman, Amjad Mohd. Rahim, Mohd. Shafry Saba, Tanzila |
author_sort |
Ebrahim, Anwar Yahya |
title |
Features selection for offline handwritten signature verification: state of the art |
title_short |
Features selection for offline handwritten signature verification: state of the art |
title_full |
Features selection for offline handwritten signature verification: state of the art |
title_fullStr |
Features selection for offline handwritten signature verification: state of the art |
title_full_unstemmed |
Features selection for offline handwritten signature verification: state of the art |
title_sort |
features selection for offline handwritten signature verification: state of the art |
publisher |
Inderscience Publishers |
publishDate |
2018 |
url |
http://eprints.utm.my/id/eprint/84622/ http://dx.doi.org/10.1504/IJCVR.2018.095590 |
_version_ |
1662754283986092032 |
score |
13.15806 |