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Indexing of Single and Multi-instance Iris Data Based on LSH-Forest and Rotation Invariant Representation

Damer, Naser and Terhörst, Philipp and Braun, Andreas and Kuijper, Arjan (2017):
Indexing of Single and Multi-instance Iris Data Based on LSH-Forest and Rotation Invariant Representation.
pp. 190-201, 7th international Conference on Computer Analysis of Images and Patterns (CAIP 2017), Ystad, Sweden, August 22.-24., 2017, ISBN 978-3-319-64697-8,
DOI: 10.1007/978-3-319-64698-5_17,
[Conference or Workshop Item]

Abstract

Indexing of iris data is required to facilitate fast search in large-scale biometric systems. Previous works addressing this issue were challenged by the tradeoffs between accuracy, computational efficacy, storage costs, and maintainability. This work presents an iris indexing approach based on rotation invariant iris representation and LSH-Forest to produce an accurate and easily maintainable indexing structure. The complexity of insertion or deletion in the proposed method is limited to the same logarithmic complexity of a query and the required storage grows linearly with the database size. The proposed approach was extended into a multi-instance iris indexing scheme resulting in a clear performance improvement. Single iris indexing scored a hit rate of 99.7% at a 0.1% penetration rate while multi-instance indexing scored a 99.98% hit rate at the same penetration rate. The evaluation of the proposed approach was conducted on a large database of 50k references and 50k probes of the left and the right irises. The advantage of the proposed solution was put into prospective by comparing the achieved performance to the reported results in previous works.

Item Type: Conference or Workshop Item
Erschienen: 2017
Creators: Damer, Naser and Terhörst, Philipp and Braun, Andreas and Kuijper, Arjan
Title: Indexing of Single and Multi-instance Iris Data Based on LSH-Forest and Rotation Invariant Representation
Language: English
Abstract:

Indexing of iris data is required to facilitate fast search in large-scale biometric systems. Previous works addressing this issue were challenged by the tradeoffs between accuracy, computational efficacy, storage costs, and maintainability. This work presents an iris indexing approach based on rotation invariant iris representation and LSH-Forest to produce an accurate and easily maintainable indexing structure. The complexity of insertion or deletion in the proposed method is limited to the same logarithmic complexity of a query and the required storage grows linearly with the database size. The proposed approach was extended into a multi-instance iris indexing scheme resulting in a clear performance improvement. Single iris indexing scored a hit rate of 99.7% at a 0.1% penetration rate while multi-instance indexing scored a 99.98% hit rate at the same penetration rate. The evaluation of the proposed approach was conducted on a large database of 50k references and 50k probes of the left and the right irises. The advantage of the proposed solution was put into prospective by comparing the achieved performance to the reported results in previous works.

ISBN: 978-3-319-64697-8
Uncontrolled Keywords: Biometrics, Multibiometrics, Iris recognition, Indexing, CRISP
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Mathematical and Applied Visual Computing
Event Title: 7th international Conference on Computer Analysis of Images and Patterns (CAIP 2017)
Event Location: Ystad, Sweden
Event Dates: August 22.-24., 2017
Date Deposited: 04 May 2020 12:27
DOI: 10.1007/978-3-319-64698-5_17
Official URL: https://link.springer.com/chapter/10.1007%2F978-3-319-64698-...
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