A Brief Overview of Hybrid Schemes for Biometric Fingerprint

Template Security

Edwin T. L. Rampine and Cynthia H. Ngejane

Modelling and Digital Sciences, Council for Scientific and Industrial Research, Pretoria, South Africa

Keywords: Fingerprint, Biometric Systems, Template Protection, Cancelable Biometrics, Biometric Cryptosystem,

Hybrid.

Abstract: Biometric systems are vastly adopted and consolidated into various information and security systems.

Hence, it is vital that these biometrics-based systems be immune to attacks. Fingerprint template protection

is a critical part of fingerprint based biometric systems. A significant number of fingerprint template

protection schemes have been published. However, none of the existing protection schemes can satisfy all

security requirements for template protection. Hence more researchers are combining these single schemes

to create more robust hybrid schemes. In this paper we present an overview of some of the various proposed

hybrid schemes for fingerprint template security. We also present their general performance results. Our

goal is to briefly report on this growing interest in creating a fully secure biometric hybrid scheme, and

show some of the proposed solutions in the literature so far.

1 INTRODUCTION

The term biometrics is defined by (Jain et al., 2004)

as automatic recognition and analysis of individuals

based on their unique physical and other traits, such

as fingerprints, DNA, irises, voice patterns, facial

patterns and hand gestures. Biological as well as

behavioral biometric characteristics are obtained

during a process called enrollment, by employing

specialized sensors and unique feature extraction

algorithms to create and store biometric templates.

During the process of recognition, the system

processes a query biometric input and compares it to

the stored template, employing matching algorithms

to yield an acceptance or rejection result.

The subject of biometric template security has

gained value due to concerns about the likely misuse

of stolen templates. (Cappelli et al., 2007) reported

that the most persistent attack resulting from stolen

biometric templates is spoofing. If an attacker

manages to steal an unsecured stored templates, he

can create a biometric spoof from the template and

gain unauthorised access or deny access to

legitimate users to a system. This is also made

possible because of the limited liveness detection

capability of most biometric systems (Nagar et al.,

2008). Moreover, in instances where generic

encryption methods (like AES, RSA, etc) are used,

the comparison of biometric templates is not

performed in the encrypted domain , thus, the

templates can be vulnerable during every

recognition transaction while they are decrypted

(Nagar et al., 2008). Hence the vast research efforts

in creating alternative protection methods or

schemes to ensure biometric template security.

Biometric template protection schemes, which

are generally categorized as biometric cryptosystems

and feature transformation, are designed to have

specific security properties (Ratha et al., 2007).

These schemes provide various algorithms

attempting to secure the user's biometric data.

Deployment of a single scheme may not be

sufficiently secure to meet all security requirements.

Hence a combination of these may be required to

enhance security (Feng et al., 2008), (Liu et al.,

2014). A notable number of researchers have stated

that a single scheme which is completely secure

does not exist yet, and thus a growing interest in

creating hybrid schemes to attempt to improve the

security of biometric templates. In this paper, we

present a brief overview of some of these proposed

hybrid schemes. We report on various hybrid

schemes presented in the literature so far and

summarize some of their security performance

contributions.

The remainder of the paper is structured as

340

Rampine, E. and Ngejane, C.

A Brief Overview of Hybrid Schemes for Biometric Fingerprint Template Security.

DOI: 10.5220/0005730503400346

In Proceedings of the 2nd Inter national Conference on Information Systems Security and Privacy (ICISSP 2016), pages 340-346

ISBN: 978-989-758-167-0

follows: Sect. 2 gives a brief overview of commonly

known schemes. In Sect. 3 we list the security

properties that each scheme must fulfil. In Sect. 4,

the surveyed hybrid schemes are presented. And

before concluding in Sect. 6, Sect. 5 presents

summary of hybrid schemes performances.

2 FINGERPRINT TEMPLATE

SECURITY SCHEMES

Fingerprint template security schemes documented

in the literature can be divided into two main

categories, namely, feature transformation and

biometric cryptosystems as depicted in Figure 1

below.

Figure 1: Classification of biometric template protection

schemes.

(Ratha et al., 2001) and (Vacca, 2007) define

feature transformation as an irreversible but

repeatable distortion of a biometric template based

on a chosen transform. The biometric template

signal is distorted with the same transform at each

presentation, for enrollment and for every

verification. The transformed templates never need

to be decrypted because the matching of the

stored/enrolled templates and query templates is

performed in the transformed domain.

On the other hand, biometric cryptosystems

either create secure templates, also referred to as

secure sketches, by using cryptographic keys or

directly generating the cryptographic keys from the

enrolled biometric templates (Ulaganathan and

Baskaran, 2015). If a non-genuine authentication

query is presented, it becomes computationally hard

to reconstruct the template from the secure sketch.

Whereas, given an authentication query template

that sufficiently matches the enrolled template, it

should be easy to decode the sketch and recover the

template. A biometric cryptosystem secures the

biometric template and also handles the secure key

management (Jain et al., 2013).

2.1 Feature Transformation

(Jain et al., 2013) explained that in Feature

Transformation techniques, the biometric template

-E

) is modified with a user specific key (yt) such

that the original template is irrecoverable from the

transformed template (yt). During verification, the

same transformation is applied to the biometric

query (x

) and the matching is performed in the

transformed space to avoid recovery of the original

biometric template. Since the key (kt) needs to be in

the same storage system with (yt), the template

security is assured only if the transformation method

is non-invertible even when (kt) is compromised

(Jain et al., 2013). However, in other techniques of

feature transformation known to be invertible

transforms, the key (kt) can be used to recover the

original biometric template (Beng and Hui, 2010).

Some established examples of template

transformation include Bio-Hashing (invertible) and

cancellable biometrics (non-invertible) (Teoh et al.,

2007), (Ratha et al., 2007).

2.1.1 Bio-hashing

Bio-hashing is defined (Mwema et al., 2015) as a

biometric template security technique in which

features from a biometric template are transformed

using a transformation function defined by a

cryptographic key known only to the user. This key

or token is securely stored and remembered by the

user for subsequent authentication. However, the

performance of bio-hash can degrade and the secure

template be reverted to the original state if the

genuine token is stolen and used by the impostor to

pose as the genuine user (Teoh et al., 2008).

For any invertible transform function y = f(x),

we can derive x = f

-1

(y) where f

-1

is the inverse of f.

From the security point of view, invertible

transforms can be easily circumvented when the

transformation used is known by the attacker

(Cheung et al., 2005). There are some existing

proposals in the literature on how to improve the

non-invertibility of bio-hashing transforms. (Teoh et

al., 2008) demonstrated for instance that, the use of

multi-state bio-hash transforms can resolve the

stolen-token problem. Bio-hashing is an instance of

Biometric Salting (Rathgeb and Uhl, 2011).

(Savvides et al., 2004) denotes Biometric salting as

transforms which can be invertible.

2.1.2 Cancellable Biometrics

Cancellable biometrics, according to (Jin and Lim,

2010), refers to the methodically reproducible

A Brief Overview of Hybrid Schemes for Biometric Fingerprint Template Security

341

distortion of biometric features used to secure a

transformed biometric template. If a cancellable

transformed biometric template is compromised, the

distortion/transformation characteristics can be

replaced, and the same biometrics is mapped to a

new transformation template, which can used

subsequently (Radha and Karthikeyan, 2011).

For non-invertible transforms, non-invertibility

improves the security of the biometric template by

resetting the order or position of the feature set with

a transformation process. However, this degrades the

performance of the transformed features due to the

enlargement of intra-user variation in the biometrics.

Hence, it is challenging to create a non-invertible

transformation process that satisfies both

performance and non-invertibility (Jin and Lim,

2010).

2.2 Biometric Cryptosystem

Biometric cryptosystems techniques employ the use

of extra biometric reliant information that can be

made public referred to as helper data. This helper

data is used to either retrieve or generate

cryptographic keys. The biometric matching process

is performed by validating the generated key, where

the result of the process is either a key or a match or

no match result (Ramu and Arivoli, 2012).

(Uludag et al., 2004) stated that cryptographic

keys are long and random, they are difficult to

predict or hack. Moreover, the cryptographic keys

can be stored locally or centrally and are accessed by

authorized users only. Depending on the kind of

helper data derived, Biometric cryptosystems can be

categorized as key-binding or key-generation

systems, see Figure 2 (Ramu and Arivoli, 2012).

2.2.1 Key-binding

In key-binding, helper data is created by combining

a secret key with the biometric template. During

authentication, the cryptographic key can be

retrieved from the helper data. (Ramu and Arivoli,

2012) stated that the cryptographic keys are

revocable because they are not dependent of the

biometric information, but to update the key would

require a complete reenrollment to create new helper

data.

(Juels and Wattenburg, 1999) proposed Fuzzy

commitment scheme, which is now a well-known

example of the key binding cryptosystem.

Distributed source coding (Draper et al., 1999),

Fuzzy vault (Juels and Wattenburg, 1999) and

Reliable components schemes (Tuyls et al., 2005)

are among a number of other template protection

schemes that were proposed and can be considered

to be key binding approaches.

Figure 2: Overview concept of the Key generation

cryptosystem (a) Key-binding and (b) Key-generation

(adapted from (Ramu and Arivoli, 2012)).

2.2.2 Key-generation

In key-generation, helper data is created only and

directly from the enrolled biometric template. The

cryptographic keys are generated from the created

helper data. The proposed schemes that allow secure

keys to be generated from the helper data are fuzzy

extractors and secure sketches, both are defined

further by (Dodis et al., 2004) and (Verbitskiy et al.,

2010).

A secure sketch solves the problem of error

tolerance. It generates a random and unique bit

string from the enrolled biometric template, that, and

only reveals limited information about the enrolled

template. It also allows the exact reconstruction of

the enrolled template from any other input query

template that is sufficiently close. Due to the error

tolerance of this technique, it can only be effective

with a lower level of variance of the subsequent

query templates. A secure sketch, however, does not

address nonuniformity of inputs (Dodis et al., 2004).

On the other hand, a fuzzy extractor solves both

error tolerance and nonuniformity issues. It

generates a uniformly random bit string from the

enrolled biometric template with error tolerance. If

the subsequent query templates change but remain

close, the subsequent generated bit string remains

exactly the same (Verbitskiy et al., 2010).

During authentication, a bit string (stored as

helper data) is generated from the query biometric

template, and the stored helper data is used to

reconstruct the enrolled template bit string. If the

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distance between the query biometric template and

the enrolled biometric template is less than a

specified parameter, given a query biometric

template and the helper data from enrollment, the

user will be accepted (Li, 2009).

3 FINGERPRINT TEMPLATE

SECURITY SCHEMES

Biometric template protection schemes are designed

to try and ideally fulfil the following four properties

(Rathgeb and Uhl, 2010).

i) Diversity: the secure biometric template must

enforce privacy by not being cross-matchable

across databases.

ii) Revocability: it should be possible and

straightforward to revoke a compromised

biometric template and reissue a new one using

the same biometric data.

iii) Irreversibility: It must be computationally hard

to obtain the original biometric template from the

secure template. This property prevents an

attacker from creating a physical spoof of the

biometric trait from a stolen template.

iv) Accuracy: the biometric template protection

scheme should not degrade the recognition

performance (False Acceptance Rate and False

Rejection Rate) of the biometric system.

According to (Nagar et al., 2008), most existing

approaches fulfil the above properties partially. It is

indeed challenging to design a secure and high

performance scheme that also meets the

requirements of diversity and revocability. The

major challenge is handling the intrauser variability

in the acquired biometric data, since multiple

acquisitions of the same biometric data do not result

in the same feature set (Campisi, 2013).

4 OVERVIEW OF PROPOSED

HYBRID SCHEMES

There are limitations reported on individual

schemes. A single scheme is not sufficient to satisfy

all the template protection requirements (Chen and

Chen, 2010); (Ghany et al., 2012); (Liu et al., 2014),

(Feng et al., 2008). Due to this, there are a number

of hybrid schemes for fingerprint template security

proposed in the literature. A hybrid biometric

scheme is a combination of two or more biometric

template protection schemes. The combination of the

schemes is designed to meet more, ideally all, of the

biometric protection requirements.

The following are some of the proposed hybrid

fingerprint template protection schemes in the

literature, and are not ranked in any way.

4.1 Fuzzy Vault and Password

Hardening

(Nandakumar et al., 2007) proposed a hybrid

approach where the biometric features are hardened

using password before applying fuzzy vault

technique. During authentication, the user needs to

give both the password and the biometric data. The

fuzzy vault scheme secures the template storage by

binding the template with a uniformly random key,

but the non-uniform trait of biometric data can

reduce the vault security.

Advantages of the proposed fuzzy vault

password-based hardening hybrid technique include

revocability, prevention of cross-matching,

improved vault security and a reduction in the False

Accept Rate of the system without significantly

affecting the False Reject Rate.

4.2 Cancelable Biometrics and Secure

Sketches

(Bringer et al., 2008) introduced a hybrid scheme

composed of Cancelable biometrics and Secure

sketches. Their aim was to improve the security of

the fingerprint templates while keeping the matching

performance high. The cancelable biometrics

technique is used to perform an irreversible

transformation on biometric data, and attempt to

perform matching in the transformed domain.

According to (Bringer et al., 2008), the limitation of

this techniques is that during transformation, the

core structure of the template is modified, which

leads to reduced performance accuracy.

However, for Secure sketches, matching relies on

an error correction parameter. So by applying secure

sketch with error correction to cancelable

biometrics, allows the retention of good matching

performance. Furthermore, the security advantages

of both schemes are proved to accumulate.

4.3 Fuzzy Vault, Fuzzy Commitment

and Minutiae Descriptors

(Nagar et al., 2010) proposed a hybrid scheme which

uses the fuzzy vault scheme, fuzzy commitment

scheme and incorporating minutiae descriptors in

order to improve the recognition performance as

A Brief Overview of Hybrid Schemes for Biometric Fingerprint Template Security

343

well as the security. They incorporated minutiae

descriptors by embedding them in the vault

construction using the fuzzy commitment technique.

The minutiae descriptors capture ridge orientation

and frequency information in a minutia’s

neighborhood.

They also experimentally demonstrated that by

including the use of minutiae descriptors, the False

Match Rate is reduced exponentially without

degrading the Genuine Accept Rate significantly.

4.4 Key-generation and Cancelable

Biometrics

(Lalithamani and Soman, 2009) discusses an

effective scheme for generating irrevocable

cryptographic keys from cancelable fingerprint

templates. They initially extract minutiae points

from the fingerprint image, then apply a cancelable

biometric algorithm to get a cancelable fingerprint

template. Thereafter, an irrevocable cryptographic

key is generated from the cancelable fingerprint

template.

The security of the proposed hybrid scheme is

improved by two strong features; cancelable

transformation and irreversibility. (Lalithamani and

Soman, 2009) report that with the proposed scheme,

in a case where the secure template is compromised,

it can be cancelled and reissued with a different

transformation parameters. Moreover, it is not

possible to cross match the template across

databases. They also indicate the inherent

irrevocable nature of the scheme ensures that it is

impractical to recover the cancelable template from

the generated cryptographic key.

4.5 Fuzzy Vault and Regional

Transformation

(Chen and Chen, 2010) proposed a hybrid scheme

that combines biometric encryption (key-binding)

and feature transformation (noninvertible), which is

more secure than any single approach. For the

biometric encryption, they apply fuzzy vault using a

linear equation and chaff points on fingerprint

template. Then for the noninvertible transformation,

they apply a regional transformation for every

minutia-centered circular region. The hybrid scheme

enhances security, diversity, and revocability.

4.6 Minutiae Cylinder Code and

Random Key

A hybrid scheme combining a transformation and a

user key was proposed by (Mirmohamadsadeghi and

Drygajlo, 2013) to provide the MCC-based

fingerprint representation with improved security

properties. They used a baseline fingerprint

descriptor by employing minutiae cylinder code

which provides rotation and translation invariant

descriptors for accurate recognition. The main

benefits of this hybrid technique is revocability,

irreversibility and also reduces the size of the

resulting template by half. Even if the key is stolen,

the original biometric data remains protected

(Mirmohamadsadeghi and Drygajlo, 2013).

5 PERFORMANCE RESULTS

PRESENTATION OF VARIOUS

HYBRID SCHEMES

5.1 Evaluation of Biometric Systems

Performance

Several types of indexes are used to analyze and

evaluate biometric systems performance. The

following provides the common used indexes as

basic measures of accuracy of a biometric system.

i) False Acceptance Rate (FAR): is the estimated

probability at which a biometric sample will be

incorrectly declared to belong to the claimed

identity when it actually belongs to a different

identity (false positive) (Valencia, 2003), FAR is

also referred to as False Match Rate (FMR).

ii) False Rejection Rate (FRR): is the estimated

probability at which a biometric sample will be

incorrectly rejected as a claimed identity when it

actually belongs to that identity (false negative)

(Valencia, 2003), FRR is also referred to as False

Non-Match Rate (FNMR).

iii) Equal Error Rate (EER): is the point at which

FMR is equal to FNMR (Maio, Maltoni,

Cappelli, Wayman and Jain, 2002).

iv) Genuine Accept Rate: The Genuine Accept Rate

(GAR), also referred to as True Accept Rate

(TAR), is an alternative to FRR. It is computed

as 1 - FRR (Gamassi et al., 2004).

5.2 Biometric Hybrid Schemes

Performance

Some hybrid schemes have been experimentally

proven to reduce the FAR of a biometric system

without significantly affecting the FRR. Other

researchers presented hybrid schemes that are

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designed to allow the retention of good matching

performance. Table 1 below presents a summary of

performance results presented in the proposals of the

various hybrid schemes.

Table 1: Presentation of various Hybrid Schemes

Performance Results.

It should be noted that the results above were not

found using the same data sets by various

researchers, therefore no direct performance

comparison can be made. The aim of the

presentation of the results above is to report on the

proven retention of accuracy while improving

template security by these hybrid schemes.

6 CONCLUSION

We have presented various hybrid schemes for

fingerprint template protection. Numerous

researchers have stated the use of a single biometric

template security approach may not be enough to

meet all security requirements. Hence, hybrid

schemes continue to be developed and tested. For

each proposed hybrid scheme, experiments show

that there is an improvement in the overall

performance of the biometric system. We also listed

the security requirements that each scheme must

meet, and we finally presented the specific

performance matrices measured for the various

hybrid schemes.

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