Human Signature Verification Using Machine Vision: Statistical and neural network approaches - Softcover
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.
"synopsis" may belong to another edition of this title.
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.
"About this title" may belong to another edition of this title.
Search results for Human Signature Verification Using Machine Vision:...
Seller Image
Human Signature Verification Using Machine Vision
Published by
Noor Publishing Okt 2016, 2016
ISBN 10: 3330801549
ISBN 13: 9783330801547
New
Taschenbuch
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 116 pp. Englisch. Seller Inventory # 9783330801547
Seller Image
Human Signature Verification Using Machine Vision
Print on DemandSeller: moluna, Greven, Germany
Seller rating 4 out of 5 stars
Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: El-Faki MohammedMohammed 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 con. Seller Inventory # 151242743
Buy New
£ 37.47
£ 42.72 shipping
Ships from Germany to U.S.A.
Ships from Germany to U.S.A.
Quantity: Over 20 available
Stock Image
Human Signature Verification Using Machine Vision: Statistical and neural network approaches
Seller: Revaluation Books, Exeter, United Kingdom
Seller rating 5 out of 5 stars
Paperback. Condition: Brand New. 116 pages. 8.66x5.91x0.27 inches. In Stock. Seller Inventory # 3330801549
Buy New
£ 70.78
£ 10 shipping
Ships from United Kingdom to U.S.A.
Ships from United Kingdom to U.S.A.
Quantity: 1 available
Seller Image
Human Signature Verification Using Machine Vision
Published by
Noor Publishing Okt 2016, 2016
ISBN 10: 3330801549
ISBN 13: 9783330801547
New
Taschenbuch
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. Neuware -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.Books on Demand GmbH, Überseering 33, 22297 Hamburg 116 pp. Englisch. Seller Inventory # 9783330801547
Seller Image
Human Signature Verification Using Machine Vision : Statistical and neural network approaches
Print on DemandSeller: AHA-BUCH GmbH, Einbeck, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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. Seller Inventory # 9783330801547
Seller Image
Human Signature Verification Using Machine Vision | Statistical and neural network approaches
Print on DemandSeller: preigu, Osnabrück, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. 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: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu Print on Demand. Seller Inventory # 107755716