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Features selection for offline handwritten signature verification: State of the art

Ebrahim, AY, Kolivand, H, Rehman, A, Rahim, MSM and Saba, T (2018) Features selection for offline handwritten signature verification: State of the art. International Journal of Computational Vision and Robotics, 8 (6). ISSN 1752-9131

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Abstract

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.

Item Type: Article
Uncontrolled Keywords: 09 Engineering, 08 Information And Computing Sciences
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
Divisions: Computer Science
Publisher: Inderscience
Date Deposited: 20 Dec 2018 15:45
Last Modified: 21 Dec 2018 15:58
DOI or Identification number: 10.1504/IJCVR.2018.095590
URI: http://researchonline.ljmu.ac.uk/id/eprint/9857

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