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Off-line signature recognition using weightless neural network and feature extraction
Author
Source
The Iraqi Journal of Electrical and Electronic Engineering
Issue
Vol. 11, Issue 1 (30 Jun. 2015), pp.124-131, 8 p.
Publisher
University of Basrah College of Engineering
Publication Date
2015-06-30
Country of Publication
Iraq
No. of Pages
8
Main Subjects
Information Technology and Computer Science
Topics
Abstract EN
The problem of automatic signature recognition and verification has been extensively investigated due to the vitality of this field of research.
Handwritten signatures are broadly used in daily life as a secure way for personal identification.
In this paper a novel approach is proposed for handwritten signature recognition in an off-line environment based on Weightless Neural Network (WNN) and feature extraction.
This type of neural networks (NN) is characterized by its simplicity in design and implementation.
Whereas no weights, transfer functions and multipliers are required.
Implementing the WNN needs only Random Access Memory (RAM) slices.
Moreover, the whole process of training can be accomplished with few numbers of training samples and by presenting them once to the neural network.
Employing the proposed approach in signature recognition area yields promising results with rates of 99.67 % and 99.55 % for recognition of signatures that the network has trained on and rejection of signatures that the network .has not trained on, respectively.
American Psychological Association (APA)
al-Sayigh, Ali. 2015. Off-line signature recognition using weightless neural network and feature extraction. The Iraqi Journal of Electrical and Electronic Engineering،Vol. 11, no. 1, pp.124-131.
https://search.emarefa.net/detail/BIM-583668
Modern Language Association (MLA)
al-Sayigh, Ali. Off-line signature recognition using weightless neural network and feature extraction. The Iraqi Journal of Electrical and Electronic Engineering Vol. 11, no. 1 (2015), pp.124-131.
https://search.emarefa.net/detail/BIM-583668
American Medical Association (AMA)
al-Sayigh, Ali. Off-line signature recognition using weightless neural network and feature extraction. The Iraqi Journal of Electrical and Electronic Engineering. 2015. Vol. 11, no. 1, pp.124-131.
https://search.emarefa.net/detail/BIM-583668
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 130-131
Record ID
BIM-583668