Offline Signature Recognition Using Machine Learning

Mohammad Ikhsan, Bin Zakaria and Gunay, Karli (2012) Offline Signature Recognition Using Machine Learning. In: 3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo.

[img]
Preview
Text
1. Offline Signature Recognition Using Machine Learning.pdf

*- Download (274Kb) | Preview

Abstract

Biometric behavior can be recognized through the signature behavior of a person. It is mostly used for authorization and authentication in legal documentation papers. Signature recognition has two ways of verification, dynamic or online recognition and static or offline recognition. In this paper we use offline recognition to analyze signature images using Artificial Neural Network. We used mark minutia masking as the feature extraction. We proposed offline signature recognition using machine learning with supervised learning algorithm. The aim of using artificial neural network is to automatically find signatures that match to the owners of the signatures. Based on our evaluation, after we compared feed forward backpropagation and other supervised learning network such cascade-forward network, it revealed cascade-forward shown the highest accuracy100 % with low mean square error 0. Keywords: biometric, offline signature, machine learning

Item Type: Conference or Workshop Item (Paper)
Subjects: H Social Sciences > H Social Sciences (General)
T Technology > T Technology (General)
Divisions: Faculty of Engineering and Information Technologies > Information Technologies Department
Depositing User: Mr. Emir Cickusic
Date Deposited: 19 Oct 2012 12:46
Last Modified: 19 Oct 2012 12:46
URI: http://eprints.ibu.edu.ba/id/eprint/1142

Actions (login required)

View Item View Item