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Multi-privacy biometric protection scheme using ensemble systems.

Damasceno, M, Canuto, AMP and Poh, N (2015) Multi-privacy biometric protection scheme using ensemble systems. In: International Joint Conference on Neural Networks 2015, 2015-07-12 - 2015-07-17, Killarney, Ireland.

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Abstract

Biometric systems use personal biological or behavioural traits that can uniquely characterise an individual but this uniqueness property also becomes its potential weakness when the template characterising a biometric trait is stolen or compromised. To this end, we consider two strategies to improving biometric template protection and performance, namely, (1) using multiple privacy schemes and (2) using multiple matching algorithms. While multiple privacy schemes can improve the security of a biometric system by protecting its template; using multiple matching algorithms or similarly, multiple biometric traits along with their respective matching algorithms, can improve the system performance due to reduced intra-class variability. The above two strategies lead to a novel, ensemble system that is derived from multiple privacy schemes. Our findings suggest that, under the worst-case scenario evaluation where the key or keys protecting the template are stolen, multi-privacy protection scheme can outperform a single protection scheme as well as the baseline biometric system without template protection.

Item Type: Conference or Workshop Item (Conference Paper)
Subjects : Computer Science
Divisions : Faculty of Engineering and Physical Sciences > Computing Science
Authors :
AuthorsEmailORCID
Damasceno, MUNSPECIFIEDUNSPECIFIED
Canuto, AMPUNSPECIFIEDUNSPECIFIED
Poh, NUNSPECIFIEDUNSPECIFIED
Date : 2015
Identification Number : https://doi.org/10.1109/IJCNN.2015.7280657
Copyright Disclaimer : © 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Contributors :
ContributionNameEmailORCID
UNSPECIFIEDIEEE, UNSPECIFIEDUNSPECIFIED
Related URLs :
Depositing User : Symplectic Elements
Date Deposited : 20 Oct 2016 09:08
Last Modified : 20 Oct 2016 09:08
URI: http://epubs.surrey.ac.uk/id/eprint/812528

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