Sinossi
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.
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L'autore
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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A Spatial Invarient Model for Local Attributes of Fingerprint Ridges
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LAP LAMBERT Academic Publishing Sep 2013, 2013
ISBN 10: 3659451940
ISBN 13: 9783659451942
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Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
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Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 152 pp. Englisch. Codice articolo 9783659451942
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A Spatial Invarient Model for Local Attributes of Fingerprint Ridges
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VDM Verlag Dr. Mueller Aktiengesellschaft & Co. KG, 2013
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ISBN 13: 9783659451942
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A Spatial Invarient Model for Local Attributes of Fingerprint Ridges
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LAP LAMBERT Academic Publishing, 2013
ISBN 10: 3659451940
ISBN 13: 9783659451942
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Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Ali MelaMela 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. Codice articolo 5157005
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A Spatial Invarient Model for Local Attributes of Fingerprint Ridges
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VDM Verlag Dr. Mueller Aktiengesellschaft & Co. KG, 2013
ISBN 10: 3659451940
ISBN 13: 9783659451942
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A Spatial Invarient Model for Local Attributes of Fingerprint Ridges
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VDM Verlag Dr. Mueller Aktiengesellschaft & Co. KG, 2013
ISBN 10: 3659451940
ISBN 13: 9783659451942
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Condizione: New. PRINT ON DEMAND pp. 152. Codice articolo 18127671247
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A Spatial Invarient Model for Local Attributes of Fingerprint Ridges
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LAP LAMBERT Academic Publishing Sep 2013, 2013
ISBN 10: 3659451940
ISBN 13: 9783659451942
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Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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.Books on Demand GmbH, Überseering 33, 22297 Hamburg 152 pp. Englisch. Codice articolo 9783659451942
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A Spatial Invarient Model for Local Attributes of Fingerprint Ridges
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Taschenbuch. Condizione: Neu. A Spatial Invarient Model for Local Attributes of Fingerprint Ridges | Mela Ali (u. a.) | Taschenbuch | 152 S. | Englisch | 2013 | LAP LAMBERT Academic Publishing | EAN 9783659451942 | 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. Codice articolo 105502928
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A Spatial Invarient Model for Local Attributes of Fingerprint Ridges
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LAP LAMBERT Academic Publishing, 2013
ISBN 10: 3659451940
ISBN 13: 9783659451942
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Da: AHA-BUCH GmbH, Einbeck, Germania
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Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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. Codice articolo 9783659451942
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