Human Signature Verification Using Machine Vision | Statistical and neural network approaches

Mohammed El-Faki (u. a.)

ISBN 10: 3330801549 / ISBN 13: 9783330801547

Published by Noor Publishing, 2016

About this Item

Human Signature Verification Using Machine Vision | Statistical and neural network approaches | Mohammed El-Faki (u. a.) | Taschenbuch | 116 S. | Englisch | 2016 | Noor Publishing | EAN 9783330801547 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.

Seller Inventory # 107755716

Bibliographic Details

• Title: Human Signature Verification Using Machine Vision | Statistical and neural network approaches
• Publisher: Noor Publishing
• Publication Date: 2016
• Language: English
• ISBN 10: 3330801549
• ISBN 13: 9783330801547
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 8D millimeters 220 millimeters width by 150 millimeters height by 8 millimeters depth
• Weight: 191 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

Signature forgery still represents a great challenge to financial institutions, which makes accurate signature verification inevitable. On the other hand, computer technology and information processing areas witness remarkable qualitative improvements associated with significant costs reduction. This boosted the usage of machine vision techniques. In this research, an intensive work was carried out on offline signatures to establish a system for verifying them using their digital images. Signature morphological structure was utilized to explore characteristics associated with different signatures. Signature verification algorithms were developed using binary images of signatures employing two different verification approaches, one was based on statistical techniques, while the other was based on neural networks (NN) techniques. A signature database was built by collecting 840 signatures from 66 volunteers, and was used for training the statistical and NN classifiers and subsequently for testing purposes. Research results indicated that the statistical classifiers' outcomes were highly satisfactory whereas the NN classifiers' outcomes were not of the same quality.

About the Author

Mohammed S. El-Faki is a Prof. at King Faisal University. He got PhD. and MSc. from Kansas State University, and BSc. from Khartoum University. Research interests: pattern recognition, process automation, quality control, early detection of insects using mult-sensor fusion, water conservation, solar energy applications, date palm equipment design.