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Efficient Biometric Indexing and Retrieval Techniques for Large-Scale Systems: SpringerBriefs in Computer Science

Autor Ilaiah Kavati, Munaga V.N.K. Prasad, Chakravarthy Bhagvati
en Limba Engleză Paperback – 16 mai 2017
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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Specificații

ISBN-13: 9783319576596
ISBN-10: 3319576593
Pagini: 67
Ilustrații: XVII, 67 p. 29 illus.
Dimensiuni: 155 x 235 x 5 mm
Greutate: 0.14 kg
Ediția:1st ed. 2017
Editura: Springer International Publishing
Colecția Springer
Seria SpringerBriefs in Computer Science

Locul publicării:Cham, Switzerland

Cuprins

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.

Caracteristici

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