Ibrahim Gambo

Work place: Department of Mathematical Sciences, Faculty of Science, Universiti Teknologi Malaysia

E-mail: igambo@utm.my


Research Interests:


Ibrahim Gambo is currently a Postdoctoral fellow at the Department of Mathematical Sciences, Faculty of Science Universiti Teknologi Malaysia (UTM). He received his Doctor of Philosophy Mathematics as well as his Master degree in Mathematics from Universiti Teknologi Malaysia and his Bachelor degree of Mathematics from Bayero University Kano (BUK), Kano, Nigeria. His research interest includes Fuzzy sets theory, Algebra and its Applications in other multidisciplinary areas of Engineering. He has published quite a several refereed index journals and conference papers in the areas of his expertise.

Author Articles
Off-line Handwritten Signature Verification System: Artificial Neural Network Approach

By N. M. Tahir Adam N. Ausat Usman I. Bature Kamal A. Abubakar Ibrahim Gambo

DOI: https://doi.org/10.5815/ijisa.2021.01.04, Pub. Date: 8 Feb. 2021

Nowadays, it is evident that signature is commonly used for personal verification, this justifies the necessity for an Automatic Verification System (AVS). Based on the application, verification could either be achieved Offline or Online. An online system uses the signature’s dynamic information; such information is captured at the instant the signature is generated. An offline system, on the other hand, uses an image (the signature is scanned). In this paper, some set of simple shaped geometric features are used in achieving offline Verification of signatures. These features include Baseline Slant Angle (BSA), Aspect Ratio (AR), and Normalized Area (NA), Center of Gravity as well as the line’s Slope that joins the Center of Gravities of the signature’s image two splits. Before the features extraction, a signature preprocessing is necessary to segregate its parts as well as to eliminate any available spurious noise. Primarily, System training is achieved via a signature record which was acquired from personalities whose signatures had to be validated through the system. An average signature is acquired for each subject as a result of incorporating the aforementioned features which were derived from a sample set of the subject’s true signatures. Therefore, a signature functions as the prototype for authentication against a requested test signature. The similarity measure within the feature space between the two signatures is determined by Euclidian distance. If the Euclidian distance is lower than a set threshold (i.e. analogous to the minimum acceptable degree of similarity), the test signature is certified as that of the claiming subject otherwise detected as a forgery. Details on the stated features, pre-processing, implementation, and the results are presented in this work.

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