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Efficient biometric indexing and retrieval techniques for large-scale systems

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Efficient biometric indexing and retrieval techniques for large-scale systems/ by Ilaiah Kavati, Munaga V.N.K. Prasad, Chakravarthy Bhagvati.
Author:	Kavati, Ilaiah.
other author:	Prasad, Munaga V.N.K.
Published:	Cham :Springer International Publishing : : 2017.,
Description:	xvii, 67 p. :ill., digital ; : 24 cm.;
Contained By:	Springer eBooks
Subject:	Biometric identification. -
Online resource:	http://dx.doi.org/10.1007/978-3-319-57660-2
ISBN:	9783319576602

Efficient biometric indexing and retrieval techniques for large-scale systems
Kavati, Ilaiah.

Efficient biometric indexing and retrieval techniques for large-scale systems[electronic resource] /by Ilaiah Kavati, Munaga V.N.K. Prasad, Chakravarthy Bhagvati. - Cham :Springer International Publishing :2017. - xvii, 67 p. :ill., digital ;24 cm. - SpringerBriefs in computer science,2191-5768. - SpringerBriefs in computer science..

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.

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.

ISBN: 9783319576602

Standard No.: 10.1007/978-3-319-57660-2doiSubjects--Topical Terms:

598553
Biometric identification.

LC Class. No.: TK7882.B56 / K38 2017

Dewey Class. No.: 006.248

Efficient biometric indexing and retrieval techniques for large-scale systems
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245 1 0 $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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505 0 $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.
520 $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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