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03318nam a22005295i 4500 |
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978-3-319-57660-2 |
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DE-He213 |
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20170509163816.0 |
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cr nn 008mamaa |
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170509s2017 gw | s |||| 0|eng d |
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|a 9783319576602
|9 978-3-319-57660-2
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|a 10.1007/978-3-319-57660-2
|2 doi
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|d GrThAP
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|a 570.15195
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|a Kavati, Ilaiah.
|e author.
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|a Efficient Biometric Indexing and Retrieval Techniques for Large-Scale Systems
|h [electronic resource] /
|c by Ilaiah Kavati, Munaga V.N.K. Prasad, Chakravarthy Bhagvati.
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|a Cham :
|b Springer International Publishing :
|b Imprint: Springer,
|c 2017.
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| 300 |
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|a XVII, 67 p. 29 illus.
|b online resource.
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|a text
|b txt
|2 rdacontent
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|a computer
|b c
|2 rdamedia
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|a online resource
|b cr
|2 rdacarrier
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|a text file
|b PDF
|2 rda
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|a SpringerBriefs in Computer Science,
|x 2191-5768
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| 505 |
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|a Introduction -- Hierarchical Decomposition of Extended Triangulation for Fingerprint Indexing -- An Efficient Score-Based Indexing Technique for Fast Palmprint Retrieval -- A New Cluster-Based Indexing Technique for Palmprint Databases Using Scores and Decision-Level Fusion -- Conclusions and Future Scope.
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|a This work presents a review of different indexing techniques designed to enhance the speed and efficiency of searches over large biometric databases. The coverage includes an extended Delaunay triangulation-based approach for fingerprint biometrics, involving a classification based on the type of minutiae at the vertices of each triangle. This classification is demonstrated to provide improved partitioning of the database, leading to a significant decrease in the number of potential matches during identification. This discussion is then followed by a description of a second indexing technique, which sorts biometric images based on match scores calculated against a set of pre-selected sample images, resulting in a rapid search regardless of the size of the database. The text also examines a novel clustering-based approach to indexing with decision-level fusion, using an adaptive clustering algorithm to compute a set of clusters represented by a ‘leader’ image, and then determining the index code from the set of leaders. This is shown to improve identification performance while using minimal resources.
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| 650 |
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|a Computer science.
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| 650 |
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|a Special purpose computers.
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| 650 |
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|a Information storage and retrieval.
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| 650 |
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|a Biometrics (Biology).
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| 650 |
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|a Computer Science.
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| 650 |
2 |
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|a Biometrics.
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| 650 |
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|a Security.
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| 650 |
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|a Information Storage and Retrieval.
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| 650 |
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|a Special Purpose and Application-Based Systems.
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| 700 |
1 |
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|a Prasad, Munaga V.N.K.
|e author.
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| 700 |
1 |
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|a Bhagvati, Chakravarthy.
|e author.
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| 710 |
2 |
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|a SpringerLink (Online service)
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| 773 |
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|t Springer eBooks
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| 776 |
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8 |
|i Printed edition:
|z 9783319576596
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| 830 |
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|a SpringerBriefs in Computer Science,
|x 2191-5768
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| 856 |
4 |
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|u http://dx.doi.org/10.1007/978-3-319-57660-2
|z Full Text via HEAL-Link
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| 912 |
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|a ZDB-2-SCS
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| 950 |
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|a Computer Science (Springer-11645)
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