Efficient Biometric Indexing and Retrieval Techniques for Large-Scale Systems [Paperback]

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.

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

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 malsh