Title: Features selection for offline handwritten signature verification: state of the art
Authors: Anwar Yahya Ebrahim; Hoshang Kolivand; Amjad Rehman; Mohd Shafry Mohd Rahim; Tanzila Saba
Addresses: Department of Computer Science, Babylon University, Babylon, Iraq ' Department of Computer Science, Liverpool John Moores University, Liverpool, L3 3AF, UK ' College of Computer and Information Systems, Al Yamamah University, Riyadh, 11512, Saudi Arabia ' Media and Games Innovation Centre of Excellence (MAGICX), Institute of Human Centred Engineering, Universiti Teknologi Malaysia, 81310, Johor, Malaysia ' College of Computer and Information Sciences, Prince Sultan University, Riyadh, 11586, Saudi Arabia
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.
Keywords: handwritten signature verification; feature extraction; feature reduction methods; feature selection.
International Journal of Computational Vision and Robotics, 2018 Vol.8 No.6, pp.606 - 622
Received: 05 Jan 2018
Accepted: 18 Mar 2018
Published online: 11 Oct 2018 *