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Efficient Biometric Indexing and Retrieval Techniques for Large-Scale Systems

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  • © 2017

Overview

Authors:
  1. Ilaiah Kavati

    1. MLR Institute of Technology, Hyderabad, India

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  2. Munaga V.N.K. Prasad

    1. Institute for Development and Research in Banking Technology, Hyderabad, India

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  3. Chakravarthy Bhagvati

    1. University of Hyderabad, Hyderabad, India

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  • 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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Table of contents (5 chapters)

  1. Front Matter

    Pages i-xvii
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  2. Introduction

    • Ilaiah Kavati, Munaga V. N. K. Prasad, Chakravarthy Bhagvati
    Pages 1-20

  3. Hierarchical Decomposition of Extended Triangulation for Fingerprint Indexing

    • Ilaiah Kavati, Munaga V. N. K. Prasad, Chakravarthy Bhagvati
    Pages 21-40

  4. Efficient Score-Based Indexing Technique for Fast Palmprint Retrieval

    • Ilaiah Kavati, Munaga V. N. K. Prasad, Chakravarthy Bhagvati
    Pages 41-51

  5. A New Cluster-Based Indexing Technique for Palmprint Databases Using Scores and Decision-Level Fusion

    • Ilaiah Kavati, Munaga V. N. K. Prasad, Chakravarthy Bhagvati
    Pages 53-64

  6. Conclusions and Future Scope

    • Ilaiah Kavati, Munaga V. N. K. Prasad, Chakravarthy Bhagvati
    Pages 65-67
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Keywords

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.


Authors and Affiliations

  • MLR Institute of Technology, Hyderabad, India

    Ilaiah Kavati

  • Institute for Development and Research in Banking Technology, Hyderabad, India

    Munaga V.N.K. Prasad

  • University of Hyderabad, Hyderabad, India

    Chakravarthy Bhagvati

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