Overview
- Presents an efficient indexing approach using minutiae triplets for biometric databases
- Describes a score-based indexing technique that demonstrates a decreased retrieval time and enhanced identification performance compared to other match score-based approaches
- Introduces an efficient clustering-based indexing technique, using an adaptive clustering approach to the selection of sample images to make the system suitable for large-scale applications
- Includes supplementary material: sn.pub/extras
- Includes supplementary material: sn.pub/extras
Part of the book series: SpringerBriefs in Computer Science (BRIEFSCOMPUTER)
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About this book
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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Keywords
Table of contents (5 chapters)
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Front Matter
Authors and Affiliations
Bibliographic Information
Book Title: Efficient Biometric Indexing and Retrieval Techniques for Large-Scale Systems
Authors: Ilaiah Kavati, Munaga V.N.K. Prasad, Chakravarthy Bhagvati
Series Title: SpringerBriefs in Computer Science
DOI: https://doi.org/10.1007/978-3-319-57660-2
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: The Author(s) 2017
Softcover ISBN: 978-3-319-57659-6Published: 16 May 2017
eBook ISBN: 978-3-319-57660-2Published: 09 May 2017
Series ISSN: 2191-5768
Series E-ISSN: 2191-5776
Edition Number: 1
Number of Pages: XVII, 67
Number of Illustrations: 29 b/w illustrations
Topics: Biometrics, Security, Information Storage and Retrieval, Special Purpose and Application-Based Systems