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...

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Main Authors: Ebrahim, Anwar Yahya, Kolivand, Hoshang, Rehman, Amjad, Mohd. Rahim, Mohd. Shafry, Saba, Tanzila
Format: Article
Published: Inderscience Publishers 2018
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Online Access:http://eprints.utm.my/id/eprint/84622/
http://dx.doi.org/10.1504/IJCVR.2018.095590
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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
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score 13.15806