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A Spatial Invarient Model for Local Attributes of Fingerprint Ridges

Ali Mela

ISBN 10: 3659451940 / ISBN 13: 9783659451942
Published by LAP Lambert Academic Publishing
New Condition: New Paperback
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About this Item

Paperback. 152 pages. Dimensions: 9.0in. x 6.0in. x 0.3in.Fingerprint recognition and verification are always the key issues in intelligent technology and information security. For a considerable fraction of population, the representation based on complete detection of ridge structures in the fingerprint is difficult to be accomplished. The main challenge is partial loss of the ridge structures. In this work, we developed a geometrically based method for fingerprint recognition and verification tasks; a set of partial local features extracted from fingerprint ridges, minutia, and pores attributes are used. The proposed system passes through two main phases: Training phase and Test phase. In the training phase, the system is trained using a set of low quality fingerprint images to select the best discriminating local features which can lead to best recognition rates. During the test phase, the system performance is examined to know the cognitive power of the system. This item ships from multiple locations. Your book may arrive from Roseburg,OR, La Vergne,TN. Bookseller Inventory # 9783659451942

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Bibliographic Details

Title: A Spatial Invarient Model for Local ...

Publisher: LAP Lambert Academic Publishing

Binding: Paperback

Book Condition:New

Book Type: Paperback

About this title

Synopsis:

Fingerprint recognition and verification are always the key issues in intelligent technology and information security. For a considerable fraction of population, the representation based on complete detection of ridge structures in the fingerprint is difficult to be accomplished. The main challenge is partial loss of the ridge structures. In this work, we developed a geometrically based method for fingerprint recognition and verification tasks; a set of partial local features extracted from fingerprint ridges, minutia, and pores attributes are used. The proposed system passes through two main phases: Training phase and Test phase. In the training phase, the system is trained using a set of low quality fingerprint images to select the best discriminating local features which can lead to best recognition rates. During the test phase, the system performance is examined to know the cognitive power of the system.

About the Author:

Mela G. Abdul-Haleem, she was born in Germany 1981, obtained B.Sc. in Computer Science at College of Science/Baghdad University (2002),and M.Sc. in Computer Science at College of Science/Baghdad University (2013). Now, she is Assistant Lecturer at College of Science/Baghdad University. Research interests: image processing, and pattern recognition.

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