Biometrics is widely used in access control systems, but it has a serious drawback in case of stealing or leakage, therefore, in the recent past, biometric template security has obtained increasing interest and many solutions have been investigated to secure biometric templates. Cancelable biometrics, which consists in the application of an intentional and repeatable modification to the original biometric template, is one the most promising approach which can satisfy the required properties of non-reversibility, accuracy, diversity and reusability. One of the most promising cancellable biometrics formulation is known as BioHashing and is based on the combination of the biometric template with user-specific tokenised random numbers (generated by an hash key) to produce a non-invertible hash code. The Hashed code is irreproducible without presenting both the biometric data and the hash key, it is irreversible and it obtains extremely good performance when the hash key is kept secret. Unfortunately, its performance strongly degrades when an impostor steals the hash key and tries to authenticate as a genuine user. In the literature, several methods have been recently proposed to solve this drawback, including an improved version of BioHashing and a method named BioPhasor. In this chapter we discuss the existing literature about various biometric template protection techniques and propose some solutions to answer to one of the main lacks of existing approaches: the loss of accuracy with respect to their analogous non-protected biometric systems. Our solutions are based on the use of ensembles of classifiers in an improved version of the BioPhasor hashing and in a method for the fusion between the BioPhasor and the Improved BioHashing approaches. Moreover we propose a cohort normalization method for both protected and unprotected matchers. Our results, obtained on different datasets and biometrics, show that the proposed solutions outperform the base BioPhasor method, in particular in the worst testing hypothesis when always an impostor steals all the personal information of a genuine user.

Cancellable Biometrics: Problems and Solutions for Improving Accuracy

NANNI, LORIS;LUMINI, ALESSANDRA
2010

Abstract

Biometrics is widely used in access control systems, but it has a serious drawback in case of stealing or leakage, therefore, in the recent past, biometric template security has obtained increasing interest and many solutions have been investigated to secure biometric templates. Cancelable biometrics, which consists in the application of an intentional and repeatable modification to the original biometric template, is one the most promising approach which can satisfy the required properties of non-reversibility, accuracy, diversity and reusability. One of the most promising cancellable biometrics formulation is known as BioHashing and is based on the combination of the biometric template with user-specific tokenised random numbers (generated by an hash key) to produce a non-invertible hash code. The Hashed code is irreproducible without presenting both the biometric data and the hash key, it is irreversible and it obtains extremely good performance when the hash key is kept secret. Unfortunately, its performance strongly degrades when an impostor steals the hash key and tries to authenticate as a genuine user. In the literature, several methods have been recently proposed to solve this drawback, including an improved version of BioHashing and a method named BioPhasor. In this chapter we discuss the existing literature about various biometric template protection techniques and propose some solutions to answer to one of the main lacks of existing approaches: the loss of accuracy with respect to their analogous non-protected biometric systems. Our solutions are based on the use of ensembles of classifiers in an improved version of the BioPhasor hashing and in a method for the fusion between the BioPhasor and the Improved BioHashing approaches. Moreover we propose a cohort normalization method for both protected and unprotected matchers. Our results, obtained on different datasets and biometrics, show that the proposed solutions outperform the base BioPhasor method, in particular in the worst testing hypothesis when always an impostor steals all the personal information of a genuine user.
Methods, Applications and Analyses
153
166
L. Nanni; A. Lumini
File in questo prodotto:
Eventuali allegati, non sono esposti

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11585/96805
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 7
  • ???jsp.display-item.citation.isi??? 3
social impact