(IJACSA) International Journal of Advanced Computer Science and Applications,Vol. 10, No. 11, 2019

Effective Combination of Iris-based Cancelable
Biometrics and Biometric Cryptosystems
Osama Ouda1

Norimichi Tsumura2

Department of Computer Science,
Department of Information andCollege of Computer andComputer Sciences, Chiba University,
Information Sciences, Jouf University,
1-33, Yayoi-cho, Inage-ku,
Sakaka, Al-Jawf, Saudi Arabia
Chiba, 263, JAPAN

Abstract—The fuzzy commitment scheme (FCS) is one of themost effective biometric cryptosystems (BCs) that provide securemanagement of cryptographic keys using biometric templates. In
this scheme, error correcting codes (ECCs) are firstly employed
to encode a cryptographic key into a codeword which is thensecured via linking (committing) it with a biometric template of
the same length. Unfortunately, the key length is constrained by
the size of the adopted biometric template as well as the employed
ECC(s). In this paper, we propose a secure iris template protection
scheme that combines cancelable biometrics with the FCS inorder to secure long cryptographic keys without sacrificing therecognition accuracy. First, we utilize cancelable biometrics toderive revocable templates of large sizes from the most reliable
bits in iris codes. Then, the FCS is applied to the obtainedcancelable iris templates to secure cryptographic keys of thedesired length. The revocability of cryptographic keys as wellas true iris templates is guaranteed due to the hybridization ofboth techniques. Experimental results show that the proposed
hybrid system can achieve high recognition accuracy regardlessof the key size.
Keywords—Biometric template protection; cancelable biometrics; biometric cryptosystems; BioEncoding; fuzzy commitmentI.

I NTRODUCTION

In the last few years, the marriage between biometrics andcryptography has been proven to be an effective approach toaddress several issues inherent to both technologies [1]. In spiteof the usability advantages exhibited by biometrics, several security and privacy concerns have been raised about employing
biometric characteristics in identity verification/identification.
First, biometric traits are limited and therefore revoking andreplacing a compromised biometric trait is not as easy as
canceling and replacing a compromised password or token in
traditional authentication/identification systems. Second, biometric traits are permanently associated with individuals andhence some of their private information could be revealed toadversaries if these traits are disclosed [2].
On the other hand, achieving secure management of cryptographic keys is one of the most difficult problems in cryptography. Whereas short keys are not secure, it is difficult to remember and manage several long cryptographic keys. Althoughuser-specific tokens or cards can be employed to store long
cryptographic keys, keeping such user-specific tokens secure
is not assured.
Fortunately, it turned out that integrating some cryptographic concepts into biometrics systems provides practical so-

Toshiya Nakaguchi3
Center for Frontier Medical Engineering,
Chiba University,
1-33, Yayoi-cho, Inage-ku,
Chiba, 263, JAPANlutions to the above-mentioned issues. This integration can takeone of two main forms; namely, cancelable biometrics (CB)[3] and biometric cryptosystems (BCs) [4]. CB schemes, suchas BioHashing [5], BioEncoding [6] and distorting transforms[7], aim to derive several protected templates from the original(unprotected) biometric signal employing one-way transforms.
The derived templates should be revocable, renewable, noninvertible and preserve the discriminability of original templates.
Basically, the CB construct is inspired from the concept ofone-way hash functions in classical cryptography. However,unlike cryptographic hash functions, CB can derive similarprotected templates from original biometric signals that belong
to the same user. In fact, due to the intra-user variations, one
should not expect that biometric samples acquired from thesame user to be identical. That is why most of the existing
CB techniques cannot satisfy all the requirements of the
CB construct simultaneously without integrating other userspecific authentication factors in the transformation process.
Moreover, although CB systems can exhibit a practical solution
to the problems of template protection and privacy invasion,
they are still vulnerable to some attacks that are inherent
to conventional biometric systems. For example, current CB
systems are neither resilient to Trojan horse attacks nor tooverriding Yes/No response attack.
On the other hand, BCs, such as fuzzy extractors [8],the fuzzy vault scheme (FVS) [9] and the fuzzy commitment
scheme (FCS) [10], bind/extract user-specific keys to/from
biometric templates such that the key is released only if agenuine biometric sample is presented at the time of verification. In fact, this construct can be employed to protect biometric templates as well as to provide a practical approach to
manage cryptographic keys. Unfortunately, although different
cryptographic keys can be extracted from or linked to the same
biometric template in a way that allows users to log on manysystems using only their irises or fingerprints, BCs are notdesigned to be revocable with respect to biometric templates[11]. In other words, in BCs, it is possible to replace acompromised key and bind another key to the same biometric;
however, if the biometric itself is compromised, it would not
be possible to revoke it.
In order to benefit from the advantages of both approaches,several attempts of combining both CB and BCs have been
proposed recently [12]-[22]. Most of these attempts employ the
FCS as the BC of choice due to its simplicity and efficiency.
The FCS utilizes single or concatenated ECCs to correct errors

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(IJACSA) International Journal of Advanced Computer Science and Applications,Vol. 10, No. 11, 2019
existing in different biometric samples acquired from the sameuser. Hence, the recognition accuracy of such hybrid systems
relies primarily on the correction capability of the employed
ECC(s) as well as the error rates of the adopted biometriccharacteristic. Moreover, the length of the key to be linked
with a biometric template is constrained by both the size ofthat template and the used ECC(s). Therefore, to develop an
effective hybrid template protection system using the FCS, allof the above issues need to be addressed.
In this paper, we propose a novel hybrid template protection
system that combines both CB and BCs effectively to protect
iris templates as well as cryptographic keys at the same time.
A cancelable iris template (BioCode), of any desired length, is
firstly generated from the most reliable bits in a true iris template using a new variant of our previously proposed cancelable
transformation scheme [6]. Then, a cryptographic key is linked
to the derived cancelable template employing the FCS. Iris is
one of the most accurate and reliable biometric characteristics
that has been successfully implemented in many real world
applications with very low false rejection and acceptance rates[23]. Moreover, thanks to the proposed BioEncoding-based
cancelable transformation method, the suggested hybrid system
exhibits the following advantages over other existing hybrid
template protection techniques: (1) no user-specific data need
to be used with the proposed hybrid system (i.e., the proposedsystem is tokenless), (2) no restrictions are imposed on the sizeof the key to be secured using the proposed system, (3) bothkeys and iris templates could be revoked and replaced easily
in case of compromise, and (4) a perfect recognition accuracy(0% ERR) can be achieved regardless of the key size.
The remainder of this paper is organized as follows. A
review of the related works is presented in Section II. The
FCS is reviewed in Section III. In Section IV, base BioEncoding is revisited and the proposed variant of BioEncoding
is described. The proposed hybrid template protection scheme
is presented in Section V. Experimental results and security
analysis are presented in Sections VI and VII, respectively.
Finally, conclusions are drawn in Section VIII.
II.

R ELATED W ORK

Several hybrid template protection schemes have been
proposed in the last few years. Most of these systems utilize
either the FVS or the FCS in the key binding step. On the other
hand, the main difference between the cancelable transforms
employed in these techniques lies in their commitment to theinvertibility property.
Liu et al. [12] suggested to combine random projection
based cancelable biometrics and the fuzzy vault technique toimprove the security of palm-prints recognition. Sandhya andPrasad [13] utilized the fuzzy commitment scheme to protect
cancelable templates constructed using Delaunay triangles
from fingerprint minutiae. Kanade et al. [14]-[17] proposedseveral variants of a hybrid template protection scheme that
depends on the FCS for binding cancelable iris and/or facetemplates to cryptographic keys. For obtaining cancelable templates, they suggested to shuffle bits in the original biometrictemplates using user-specific shuffling keys. The problem withthese techniques lies in their utilization of invertible cancelable
transforms rather than non-invertible ones. Thus, once thesetransforms are disclosed to adversaries, the original templates
would be obtained easily.
Wang and Plataniotis [18] proposed a two-stage hybrid
scheme for face biometrics. At the first sage, cancelable facetemplates are generated by quantizing the distance vectors
between the extracted Principal Component Analysis (PCA)feature vector and pairs of user-dependent random vectors.
Then, the FVS is utilized to bind a randomly generated key
with the obtained cancelable face template. The same approach
is followed by Nandakumar et al. [19] for protecting fingerprint
templates. At first, a user-specific password is utilized toderive a random transformation function that is applied to
the fingerprint template. Then, the transformed template issecured using the FV framework. Teoh et al. [20] proposeda two-step technique to derive personalized cryptographic
keys from the face biometric. In the first step, a cancelable
template (FaceHash) is generated from original face templateusing BioHashing (i.e., random projection followed by simple
thresholding). Then, in the second step, a cryptographic key
is derived from the generated FaceHash via Shamir’s secretsharing approach. A similar technique is presented by Song etal. [21] but for fingerprint templates. A cancelable fingerprint
template, referred to as FingerHash, is firstly generated using
BioHashing and then linked via the FCS to a cryptographic key
that is encoded using a Reed-Solomon code. More recently,
Feng et al [22] proposed a three-stage hybrid algorithm forface templates. Cancelable templates are generated at thefirst stage using random projection. At the second stage,a discriminability-preserving (DP) transform is applied tocancelable templates in order to enhance the discriminabilityof the original feature templates as well as converting theresulting cancelable template into a binary string. Finally,
the binarized cancelable template is linked to a randomly
generated binary string (the key) using the FCS. Although
all the above-mentioned techniques employ non-invertible cancelable transforms, they utilize user-specific random numbersin the cancelable transformation process. That is, they sufferfrom the same problems associated with traditional-based
authentication systems. In other words, if these random keys
are compromised, the False Acceptance Rate (FAR) would
increase significantly [24]. Moreover, although the employed
non-invertible transforms are much harder to be reversed
compared to invertible transforms, it would be much simpler
for a skilled attacker to invert the transform and disclose theoriginal features if he/she could gain access to these userspecific factors.
Unlike other CB methods, BioEncoding satisfies all therequirements of the CB construct yet does not require any
user-specific passwords/keys to be employed in the cancelable
transformation process. On the other hand, unlike the FCS, theFVS suffers from a number of security vulnerabilities [25] aswell as some implementation difficulties. Therefore, we believethat combining both BioEncoding and the FCS into a hybrid
template protection system would address most of the issues
associated to other existing hybrid techniques. However, as
mentioned earlier, some issues inherent to the FCS need to
be dealt with. Addressing these issues is the main goal of
the new BioEncoding-based cancelable transformation method
proposed in this work.

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(IJACSA) International Journal of Advanced Computer Science and Applications,Vol. 10, No. 11, 2019

Fig. 2. Cancelable transformation of base BioEncoding.

Fig. 1. Illustration of the Fuzzy Commitment Scheme.

III.

F UZZY C OMMITMENT S CHEME

The FCS is one of the most attractive BCs that have been
proposed so far. Due to its simplicity and efficiency, it has
been applied to several biometric traits by many scientificgroups [26]-[33]. As depicted in Fig. 1, the FCS requires abiometric template, t, to be represented as a binary string.
Hence, a non-binary templates need to be binarized via an
optional binarization module into a binary template, tb , before
the FCS can be applied. For iris biometric, since iris features
are commonly represented as an ordered binary string, known
as an iris code, the binarization step is not required (and hence
tb would be identical to t in Fig. 1). The FCS works asfollows. On enrollment, a random binary key K is generated
and encoded using an appropriate ECC(s) into a codeword Cof length n = ktb k. Both the binary template and the encoded
key are then XORed to produce a biometrically encrypted key
Kbio , also called a biometric key, as follows:
Kbio = C ⊕ tb

(1)

Furthermore, the hash value of the random key H(K) is
computed and stored with the biometric key in a central storage
or a user-specifictoken. At the time of verification, a binary0
template tb is extracted from a live biometric sample captured
from the person being verified and XORed with the stored
biometric key to obtain a possibly corrupted codeword C 0 :C 0 = t0b ⊕ Kbio

(2)

The obtained codeword is decoded using the ECC(s) employedon enrollment to get the verification key K 0 . Finally, the hashvalue of the recovered key, H(K 0 ) is computed using the samehashing function employed on enrollment and compared tothe stored hash value, H(K). Only if the two hash values
are identical, the key is released; otherwise, the authentication
process fails.
IV.

G ENERATING C ANCELABLE T EMPLATES

To bind long cryptographic keys with biometric templates,
the size of such templates should be as large as possible.
If (cancelable) biometric templates of any size could be
generated, keys of unconstrained lengths would be employedin the key-binding phase. Unfortunately, cancelable biometrics
techniques, such as BioHashing and BioEncoding, derive compact revocable templates from original biometrics templates. In
BioHashing, if the generated cancelable template is not shorter
than the original one, the transform could be inverted easily[34]. Also, the protected BioCodes generated using BioEncoding, as proposed in [6], are shorter than their corresponding
original templates.
In this paper, we propose a cancelable transformation
method, based on BioEncoding, which can derive protected
templates of any desired lengths from binary biometric data.
In this section, we first give a brief overview of the base
BioEncoding cancelable transformation scheme and then we
describe the proposed variant.
A. Base BioEncoding
In BioEncoding, as illustrated in Fig. 2, bits in the true
template are grouped into n/m m-bit words, where n is the
bit-length of the original (unprotected) template. At the sametime, a binary string S of length 2m is randomly generated.
Each word in the true iris code is mapped to a bit value in
S located at the position addressed by the value of that word.
For example, the first word ‘1111’ in the true template, shownin Fig. 3, is mapped to the bit value located at position 15(= 1111b ) in S, i.e. ‘0’. The l (= n/m) addressed bit values
constitute the cancelable template (BioCode).
The most important advantage of BioEncoding over other
CB methods is that it does not require the random sequence
S to be neither unique nor secret. In other words, S need not
be user specific as in BioHashing [5] and distorting transforms[7], for example. This is because even if the same sequence is
employed with all users, different BioCodes will be generated
due to the randomness that exists between iris codes generated
from different eyes. In [34], BioEncoding was comparedexperimentally to BioHashing and the obtained results showedthat BioEncoding, unlike BioHashing, does not deteriorate therecognition accuracy of the original biometric system evenunder the stolen-token scenario. Furthermore, the transform
is non-invertible due to its many-to-one nature. Therefore,
attackers would not be able to invert the transform, even if botha protected BioCode and S are known [6], [34]. In addition,
BioEncoding offers
significantly high renewability capacity. Inmfact, there are 22 different binary strings that can be addressedusing address words of length m. Therefore, a compromised

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In this paper, we propose to map each address word ofsize m1 in the true template to one out of two binary wordsof size m2 , where m2 ≥ 1. Fig. 3 shows an example wherem1 = m2 = 4. This approach exhibits two major advantages
over base BioEncoding: 1) increased renewability capacity
since different variations of word lengths and forms can be
employed, and 2) more flexibility in choosing the length of theresultant BioCode since the length of the protected BioCode
could be decided at will. The length l of the resultant BioCode
could be decided according to the following formula:l = (n × m2 )/m1
Fig. 3. Improved BioEncoding: bits replaced by words in S.

BioCode can be revoked and replaced easily by a new BioCode
generated using a different string. Moreover, different random
strings can be employed in different applications in orderto guarantee diversity that hinders attackers from attacking
different databases using cross-matching. Finally, with respect
to the recognition accuracy, the performance of the original irisrecognition system could be preserved if the random string
S satisfies the following two conditions [6]: 1) has equal
number of zeros and ones, i.e. balanced, and 2) totally different
words in the true template, such as the words ‘0000’ and‘1111’ shown in Fig. 2, address different bit values in S. In
order to ensure that a randomly generated string satisfies these
conditions, it should be tested before adopting it using thefollowing formula:f (S) =

m
m
2X
−2 2X
−1

[1 ⊕ (Si ⊕ Sj )].dh (wi , wj )(3)

i=0 j=i+1

where m is the length of any address word in the true template,
Si and Sj are the ith and jth bits in S, wi and wj are
their corresponding address words, respectively; ⊕ is the XOR
Boolean operator, and dH stands for the hamming distance
between the address words wi and wj .
Strings that have many similar bits addressed by different
words in the true templates would have large f values andtherefore should be avoided since they may decrease thediscriminability between the generated BioCodes. However, it
should be noted that although satisfying these conditions isnecessary to preserve the recognition accuracy of the original
unprotected system, it would restrict the renewability capacity
since only 2m binary strings of length 2m can strictly satisfy
the above-mentioned conditions.
B. Modified BioEncoding
The basic idea behind BioEncoding lies in mapping multiple address words in the true template to one of two values;
namely ‘0’ or ‘1’. This many-to-one transformation is necessary to achieve the non-invertibility property required by CB.
However, this does not necessarily mean that the random string
S must consist of a sequence of 0’s and 1’s. Rather, any two
distinct values could be used instead. For example, adopting asuitable matching technique in the transform domain, S might
consist of two different words such as ‘black’ and ‘white’, twodifferent symbols such as ‘+’ and ‘-’, or even two different
binary words such as ‘1010’ and ‘0101’.

(4)

Specifically, the second advantage is very important to our proposed hybrid template protection scheme since long BioCodes
will allow for linking long enough keys as described in thenext section.
V.

P ROPOSED H YBRID S YSTEM

This section presents the proposed hybrid template protection system for securing cryptographic keys using arbitrary
length protected iris codes. Fig. 4 illustrates the steps involvedin both the enrollment and verification modules of the proposedsystem. The two modules are described in detail in the next
subsections.
A. Enrollment
The enrollment module of the proposed system consists of
two concurrent procedures. As shown in Fig. 4, the first procedure is responsible for preparing a revocable, non-invertible
and protected iris template of arbitrary length np from anumber of true iris codes of length no . On the other hand, thegoal of the second procedure is to employ ECC(s) to encode
an l-bit cryptographic key into an np -bit encoded string. Bothprocesses are described below.
1) Protected templates generation: A practical biometric
cryptosystem should be able to achieve high recognition accuracy, secure long-enough cryptographic keys and be robust
against disclosure attacks of biometric templates. Here, weshow how our proposed approach of integrating BioEncodingwith the FCS can fulfill the above three requirements.
a) Extracting the most consistent bits for perfect recognition accuracy: Obviously, the recognition accuracy of theoriginal biometric system has a significant impact on theperformance of any biometric cryptosystem. Therefore, if thegenuine and imposter distributions of the unprotected irisrecognition system are not separated, the performance of the
proposed hybrid system would be far from perfect. Generally,
the only way for a biometric cryptosystem to achieve perfectaccuracy is to have a clear separation between the genuine
and imposter distributions of the original biometric system.
Although iris is considered one of the most accurate biometric
traits, such separation cannot be obtained using available
public iris datasets. In fact, the near-to-perfect performance
reported in some literature, such as the results reported by
Hao et al. in [26] for example, is mainly due to the highquality iris images of the private dataset employed in theirexperiments (only 3 out of 630 authentic samples, employedin [26], have a relatively high bit-error rates compared to thecorrection capability of the employed coding mechanism).

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Fig. 4. Proposed hybrid template protection system.

Employing public iris datasets, a clear separation between
the genuine and imposter distributions can be obtained by considering only the most consistent bits in iris codes. Consistent
bits are bits that do not tend to change their values acrossiris codes generated from different images captured from thesame eye. Hollingsworth et al. [35] showed empirically thatconsidering only consistent bits in iris codes can improve therecognition accuracy significantly.

Therefore, in this work, a number p of iris images are
acquired from a user’s eye at enrollment and iris codes are
generated from these images using the well known Daugman’salgorithms for iris recognition [36]. Then, for all enrolled eyesin the dataset, different percentages of consistent bits (e.g. themost 50%, 40%, etc. consistent bits) are identified and extracted for each class. The genuine and imposter distributions
are obtained for each percentage and the separability between
the two distributions is measured using the decidability metricd0 [36]:d0 = q|µi − µg |(5)(σi2 + σg2 )/2

where µi and µg are the means and σi2 and σg2 are the variancesof the imposter and genuine distributions, respectively.
Based on the obtained d0 values, we adopt the largest
percentage of bits that achieves a clear separation between the
genuine and imposter distributions. That is, rather than applying BioEncoding on the entire iris codes, protected BioCodes
are derived from the subset of bits that gives a clear separation
between the two distributions and hence a perfect recognition
accuracy in the original domain can be obtained.
Consistent bits are extracted by first aligning the p enrollment iris codes and then searching for bits that do notchange their values across these codes. This can be done besumming up the corresponding bits and then sorting themaccording to the sum results. Bits that sum to 0 or p arereferred to as perfectly consistent bits. For a specific numberof bits nc < no , if the number of perfectly consistent bits iswww.ijacsa.thesai.org

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larger than nc , we randomly select only nc bits from them;otherwise, we proceed with bits that sum to 1 or p − 1, 2 orp − 2, and so on, until we get a number of bits that is equal
to or greater than the specified number nc . The separation
between the resulting genuine and imposter distributions isthen investigated. If the two distributions are separated, we
adopt this number or percentage of consistent bits; otherwise
a smaller number of consistent bits is tested until the required
separation is reached. The positions of these consistent bits in
the iris code of user i are collected in a position vector Pi
and stored in the centralized storage to be used at the time ofverification.
b) Generating arbitrary length BioCodes: In order togenerate non-invertible BioCodes of any desired length, twoconsecutive permutation and BioEncoding rounds are applied
to the extracted consistent bits. The role of the first round is toobtain protected templates, which we refer to as intermediate
BioCodes, of lengths that are much larger than the length ofthe original templates in order to make it possible for the final
BioCodes (obtained after the second round) to be linked withlong cryptographic keys.
For security reasons, which are described below, theseintermediate BioCodes cannot be linked directly to cryptographic keys using the FCS construction. Therefore, to makethe proposed hybrid system robust against attacks that exploit
employing the ECCs in biometric cryptosystems to retrieve
the bound/generated key [37], a second round is required torandomize any information that might be inferred from theintermediate BioCode by deriving a random and non-invertible
template, called the final BioCode, from the intermediate
BioCode.
In the first round, the extracted consistent bits are firstly
permuted using a random permutation key and then BioEncoded using the modified BioEncoding scheme proposed in
Section III. This permutation step is necessary for two reasons.
The first is to diminish the local correlation that may exist
among the extracted consistent bits. Experiments conductedby Daugman [36] on a large iris dataset showed that only 249
degrees of freedom exist in 2048-bit iris codes. Therefore,
permuting the extracted consistent bits is necessary for randomizing those bits and increasing their entropy. The second
reason is to secure the protected BioCodes against correlation
attacks [38]. One important drawback of BioEncoding is that
although it is noninvertible for a single protected template,it might be possible to recover the original iris template bycorrelating several protected templates created from the sameiris. Therefore, it is important to change the value of address
words in original templates before applying BioEncoding inevery new application to hinder attackers from exploiting thistype of attacks. Permuting bits in the true iris codes employing
different permutations in different applications is a simple yet
efficient way to achieve this objective [38].
After obtaining the permuted bits, the modified BioEncoding is applied to extract the intermediate BioEncoded template.
As described in Section III, using this version of BioEncoding,protected BioCodes of an arbitrary length ni can be generated
from the nc consistent bits. Assuming that the true consistent
bits are grouped into address words of size m1 , the size r of
any word in the random string S can be calculated as follows:r = ni × m1 /nc(6)

As will be described later, in this work, the Hadamard ECC
is employed to encode the randomly generated cryptographic
key. Therefore, unfortunately, the obtained intermediate template cannot be linked directly with the encoded key. This isbecause it is possible to fully recover a Hadamard codeword if
only few bits are known. Specifically, knowing only 7 bits, hecould completely break the biometric cryptosystem of Kanade
et al. [14] in which the (32, 6, 8) Hadamard ECC is employed.
To boost the correction power of the Hadamard ECC, authorsin [14] insert two zeros after every three bits in the original iris
template. Hence, there are at least 12 known bits in every 32bit Hadamard codeword. This is more than enough to recover
all the codewords and hence the entire key.
Although no bits are explicitly known in the intermediate
template obtained from the first round, a similar attack could beapplied to our system if the encoded key is committed directly
using this intermediate BioCode. Consider, for example, thatwords in the true iris template are mapped to one of thefollowing two values in S: S1 = “10110010” or S2 =“01001101” where the length of words in S is 8. For the first
Hadamard codeword, the attacker may assume that it starts
with S1 and hence the first 8 bits will be known. Although
this is not sufficient for recovering the entire codeword, thesearch space will be reduced dramatically. If, on the other
hand, the first Hadamard codeword starts with S2 , the result
will be complementary to the S1 case. The same procedure canbe applied to the remaining parts of the Hadamard codeword
at hand to reduce the search space further.
In order to hinder this type of attacks, an extra round of
random permutation and (base) BioEncoding is applied. In thisround, the resulting intermediate BioCode is first permutedusing a second random permutation key in order to increase
the system robustness against invertibiliy attacks. To realize theimportance of this permutation step, let use consider the above
example again assuming further that the resulting intermediate
BioCode is divided into 4-bit address words in the subsequent
BioEncoding step. If no permutation is applied, there would
be only two possible address words for every bit in S. Thatis, every odd-numbered bit in the resulting BioCode could be
addressed by either “1011” or “0100”. Likewise, every evennumbered bit could be addressed by either “0010” or “1101”.
It is worth noting that although the permutation key need not
be user specific, and hence the same key can be employed for
all users enrolled at the same application, different permutation
keys should be employed in different applications to prevent
correlation attacks [38].
Using base BioEncoding, bits in the permuted template aregrouped into m2 -bit words and each word is mapped to a single
bit in a binary string S of length 2m2 . The length np of theresulting (final) BioCode will be:
np = ni /m2= r × nc /(m1 × m2 )

(7)
(8)

According to the length of the consistent bit vector, extracted
from the enrollment iris codes, as well as the required length ofthe protected BioCode, different values of the BioEncoding’s(base and modified) parameters can be chosen. For example,

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TABLE I. E XAMPLES OF DIFFERENT PARAMETER SETTINGS OF
B IO E NCODING WHEN nc = 1024 AND np = 2048

1
2
3

nc
1024
1024
1024

m1
4
8
4

r
32
64
64

ni
8192
8192
16384

m2
4
4
8

np
2048
2048
2048

if it is required to generate a 2048-bit protected BioCode froman 1024-bit consistent bit vector, there would be more than
one choice for values of m1 , r, and m2 . Table I shows someexamples of these values.
2) Cryptographic Key Encoding and Linking: In this module, an l-bit cryptographic key K is randomly generated andencoded into an np -bit codeword C using the two-layer error
correcting (EC) scheme described in [26]. This concatenatedEC scheme combines Hadamard and Reed-Solomon ECCs todeal with background and burst errors in iris codes, respectively. In the first layer, K is encoded using a Reed-Solomon
ECC as follows. Bits in K are divided into kRS blocks of kbit each. This set of blocks is then represented as a messageof kRS symbols over F2k and encoded into a codeword ofnRS symbols using a (nRS , kRS , tRS ) Reed-Solomon codethat has a correction capacity tRS = (nRS − kRS )/2. In thesecond layer, each of the resulting nRS symbols is represented
as a k-bit word and encoded into a 2k−1 bit codeword usinga (2k−1 , k, 2k−2 ) Hadamard code. Such Hadamard code is
generated from a Hadamard matrix of order k − 1 and cancorrect 2k−3 − 1 erroneous bits in each codeword. The length
np of the final encoded key can be calculated as follows:
np = nRS × 2k−1

(9)The correction capability of this concatenated EC scheme
depends primarily on the values of both nRS and kRS . The
lowest correction capacity is obtained when nRS = kRS (i.e.,
only Hadamard encoding is employed). That is, the described
two-layer EC scheme can correct at least up to approximately
25% of the encoded codeword, since the correction capabilityof sole Hadamard coeds is up to 25% [39]. On the other hand,the correction capability of the two-layer scheme is increased
by increasing the difference between nRS and kRS .
In this paper, the (128, 8, 64) Hadamard code is adopted
and different Reed-Solomon codes are employed based on
the required key length as well as the intra- and inter-user
distributions of the generated BioCodes.
Finally, the resulting codeword is XORed with the generated protected BioCode to get the biometric key Kbio . At thesame time, the hash value of the key H(K) is computed using
any secure hash function. Finally, the original iris template
as well as the protected one is discarded safely and only
the biometric key along with its hash value are stored in thecentralized storage for further processing during verification.
B. Verification
At the time of verification, as illustrated in Fig. 4, a
single iris image is captured from the eye being verified andits iris code is generated using the same procedure applied

on enrollment. Using the stored position vector, the mostnc consistent bits are extracted from the generated code. It
should be noted that due to the misalignment that may befound between the enrolled images and the image captured
at verification, the generated iris code is shifted eight times
in the left and right directions, as suggested by Daugman[36], and the process of extracting the most consistent bits isrepeated after each shift. The first permutation vector, stored onenrollment, is then applied to the extracted consistent bits andthe modified BioEncoding cancelable transformation is employed to derive the intermediate protected BioCode from the
permuted original bits. Then, the second permutation followedby the base BioEncoding transformation process are applied tothe intermediate BioCode to obtain the final protected iris code.
To retrieve the secured key, the final protected code is XORedwith the stored biometric key, Kbio , and the resulting bit string,is decoded using the concatenated scheme used on enrollment.
Finally, the hash of the retrieved key, K 0 , is computed andcompared to the stored hash. Only if the two hash values
H(K) and H(K 0 ), coincide, the key is released; otherwise,
the authentication process fails.
VI.

E XPERIMENTAL R ESULTS

The publicly available CASIA-IrisV3-Interval iris images
dataset [40] was used to evaluate the proposed system. This
database contains 2639 8-bit gray scale images, with a resolution of 320 × 280 pixels, captured from 396 different classes(eyes). However, many classes in this dataset have just a small
number of images. Therefore, a subset contains 700 imagesof 70 different classes, classes that have 10 images in the
database, was used.
The open source MATLAB implementation for iris recognition provided in [41] was employed to generate 9600-bit iris
codes, together with their corresponding 9600-bit noise masks,
for all images in the selected subset. The normalized Hamming
distances between all possible iris code pairs, considering allbits in the generated iris codes and taking the noise masks into
account, were calculated using the following formula [36]:dH =k(CodeA ⊕ CodeB) ∩ M askA ∩ M askBk
kM askA ∩ M askBk(10)

where CodeA and CodeB are the two iris templates being
matched, M askA and M askB are the noise masks corresponding to CodeA and CodeB respectively and ∩ represents
the bitwise AND operation. Fig. 5(a) shows the genuine
and imposter normalized Hamming distances distributions forthe adopted iris images. A clear overlap between the two
distributions can be seen in the figure. The separation between
the two distributions measured using the decidability metric(d0 ), defined in Eq. (5), is 4.061.
The separation between the genuine and imposter distributions was checked for different numbers of consistent bits. Fig.
6 shows the decidability values that result from comparing only
consistent bits that represent different percentages (from 5% to
25% step 5%) of the entire length of an iris code (that is, 9600).
We used bits that are perfectly consistent in fewer images, i.e. 5
images, then 4 images, etc. for eyes whose perfectly consistent
bits are smaller than the tested percentage. It is clear from Fig.
6 that the decidability value decreases apparently when thenumber of the tested consistent bits exceeds 10%(960 bits) of

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(IJACSA) International Journal of Advanced Computer Science and Applications,Vol. 10, No. 11, 2019

(a)

(b)

Fig. 5. Genuine and imposter distributions for (a) true iris codes, (b) the most 1024 consistent bit vectors extracted from the tested iris codes.

the entire length of the true iris code. Therefore, we decided
to extract the most consistent 1024 (nearest power of two) bitsfrom all iris codes used in our experiments. For each class inthe dataset, the extracted 1024 consistent bits were collected ina consistent bit vector that is assigned to that class. Fig. 5(b)shows the genuine and imposter distributions resulted fromcomparing those consistent bit vectors. As shown in this figure,the statistics of both distributions imply that they are separable
enough for achieving perfect recognition accuracy.
The size of the cryptographic key to be committed securely
using a protected template of length np relies on the parametersof the employed ECCs. Hence, it is not known in advance howlong the protected template should be to reliably secure a 128bit key, for example. We began by deriving 2048-bit protected
templates from the extracted 1024-bit consistent vectors andevaluating the performance of the proposed system for all
possible key lengths that can be linked with these protected
templates using the employed EC scheme. Recall that, as
indicated from the examples in Table I, BioEncoding can be
configured in many different ways to obtain 2048-bit protected
templates from 1024-bit true templates.
As shown in Table II, employing the (64, 7, 32) Hadamard
code, it is possible to employ the derived protected templates
to commit keys of lengths up to 224-bit (N.B. 224 ÷ 7 × 64 =2048). In this case, the RS code is not used and hence thematching accuracy will be affected since no block errors arecorrected. Results in Table II shows that with the introductionof RS codes, better error rates can be obtained at the expenseof the key length. Perfect accuracy was achieved for |K| = 42.
To achieve this perfect performance for longer keys, longer
protected templates should be generated. We repeated theabove experiment, employing the same HC, using 4096bit protected keys. Protected templates of such size can beobtained simply by increasing the length r of words in the
random string employed in the first round of the protected
template generation module of the proposed hybrid system.
Based on the results obtained from the previous experiment,
shown in Table II, we began by evaluating the system performance for key length |K| = 56 and we continued checking the

Fig. 6. Decidability values that result from comparing consistent bit vectorsof different lengths (lengths represent different percentages of the entire
length of an iris code).

accuracy for longer keys until a non-perfect performance wasobserved. Table III shows that a cryptographic key of size up
to 154-bit can be committed and released perfectly (with 0%
error rates) using the proposed hybrid system when 4096-bit
protected templates were employed.
The above experiment was repeated again using protected
templates of length np = 6144. Employing the same EC
scheme, we checked the system performance for different key
sizes (starting from 168-bit keys). The obtained results, shownin Table IV, indicates that employing protected templates of thementioned size can secure cryptographic keys of long-enoughsizes (up to 280-bit).
In general, since the proposed hybrid system can generate
protected iris templates of an arbitrary length, the obtained
results indicate that the proposed hybrid system exhibits perfect recognition accuracy regardless of the size of the key
that is secured using the generated revocable iris template.
A comparison between the proposed system and a number ofrecent iris cryptosystems is shown in Table V.

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TABLE II. S YSTEM PERFORMANCE EMPLOYING HC(64, 7, 32) WHEN
np = 2048

|K|
224
210
196
182
168
154
140
126
112
98
84
70
56
42

tRS
0
1
2
3
4
5
6
7
8
9
10
11
12
13

FRR(%)
4.64
2.86
2.86
2.14
2.14
1.78
1.78
1.07
1.07
1.07
0.71
0.71
0.36
0

TABLE IV. S YSTEM PERFORMANCE EMPLOYING HC(64, 7, 32) WHEN
np = 6144

|K|
168
182
196
210
224
238
252
266
280
294

FAR(%)
0
0
0
0
0
0
0
0
0
0
0
0
0
0

tRS
36
35
34
33
32
31
30
29
28
27

FRR(%)
0
0
0
0
0
0
0
0
0
0.36

FAR(%)
0
0
0
0
0
0
0
0
0
0

TABLE III. S YSTEM PERFORMANCE EMPLOYING HC(64, 7, 32) WHEN
np = 4096

|K|
56
70
84
98
112
126
140
154
168

VII.

tRS
28
27
26
25
24
23
22
21
20

FRR(%)
0
0
0
0
0
0
0
0
0.36

FAR(%)
0
0
0
0
0
0
0
0
0

S ECURITY A NALYSISThe advantage of the proposed hybrid system over otherhybrid template protection schemes is that it can protect
cryptographic keys as well as true iris templates at the sametime. Unfortunately, because other hybrid template protection
techniques employ invertible cancelable transformations forderiving the protected biometric templates, it would be very
simple to obtain the true templates from the protected ones if
the keys, linked with the cancelable templates, are disclosed
for any reason. In the proposed system, on the other hand, if alinked key is compromised, obtaining the true iris templatefrom the protected one will be computationally very harddue to the many-to-one nature of the BioEncoding cancelable
transformation process.
As shown in Fig. 7, even if both the random string S andthe permutation key are known. Every bit in the protected
BioCode could be originated from 2m−1 different address
words in the true iris template where m is the size of any
address word (in the example shown in Fig. 7, every BioCode
bit could be originated from four different address words).
Therefore, recovering all bits in the BioCode requires 2l(m−1)
trials, where l is the BioCode length. Since l = n/m, where nis the length of the true iris template, recovering all BioCode
bits would require 2n(m−1)/m ≈ 2n , if m is large. Thatis, recovering the true template from the protected BioCode
is approximately as difficult as guessing all bits in the true
template [38]. It is important to note that although the permutation process is a reversible process, knowing the permutation
key is useless unless the (irreversible) BioEncoding process is

Fig. 7. Obtaining the true iris template from the protected one is infeasible
due to the many-to-one nature of the transform.

reversed. That is, the two permutation keys as well as therandom strings employed in our proposed system need not beuser-specific; rather, they can be treated as public data without
affecting the security of the system.
On the other hand, it is important to evaluate the systemsecurity with respect to the committed cryptographic key.
As shown in Fig. 5(b), the maximum intra-user fractional
Hamming distance is 0.3106 and therefore a perfect accuracy
can be achieved as long as the correction capability of theemployed EC scheme is larger than 31.06%. As a result, inorder to recover the committed key successfully, it is enoughfor an attacker to find a 1024-bit string which is ≤ 319bit Hamming distance from the consistent iris bit vector.
Accordingly, the key strength can be measured in terms of
the entropy of the key (E) as follows [26]:21024 = 114E = log2 
1024
318

(11)

That is, the attacker needs at least 2114 computations torecover the key successfully. It is worth noting that, however,every single computation involves two rounds of BioEncoding

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(IJACSA) International Journal of Advanced Computer Science and Applications,Vol. 10, No. 11, 2019

TABLE V. C OMPARISON WITH RECENT IRIS CRYPTOSYSTEMS

Hao et al. [26]Yang et al. [31]Lee et al. [42]Iris templates
revocable?
no
no
no

Bringer et al. [27]noKanade et al. [14]Zhang et al. [33]Ziauddin et al. [28]Chai et al. [43]

no
no
no
no

Proposed

yes

Work

Dataset/subset(# samples/# classes)
private (700/70)
CASIA ver.1 (756/108)
BERC ver.1 (990/99)
CASIA Ver.1 (756/108)
ICE 2005 (2953/244)
ICE 2005 (2953/244)
private (348/128)
Bath (500/25)
CASIA ver3-Interval (868/124)
CASIA ver3-Interval
subset(700/70)

and transposition processes followed by and XORing operation
with the biometric key. This implies that it would be computationally very expensive, and might be infeasible, to obtainthe cryptographic key committed using our proposed hybrid
template protection scheme.
VIII.

C ONCLUSION AND D ISCUSSION

This paper presented a new hybrid template protection
scheme for protecting iris codes as well as securing cryptographic keys. A novel cancelable transformation method,
based on BioEncoding, has been proposed to derive cancelable
iris templates of any desired size form the most consistent
bits in original iris codes. The derived cancelable templates
were employed to secure cryptographic keys using the fuzzycommitment scheme. The proposed iris cryptosystem exhibits
four major advantages over other existing systems. First, allparameters and variables employed either in the cancelable
transformation process or in the binding process need not beuser-specific and therefore the proposed scheme is tokenless.
Second, thanks to the proposed cancelable transformation
method, the presented iris cryptosystem can secure cryptographic keys of an arbitrary length. Third, since cryptographic
keys are secured using cancelable iris templates rather thanoriginal ones, the revocability requirement is satisfied forboth cryptographic keys and iris templates. Finally, thanks to
extracting the most consistent bits from the true iris codes,
experimental results showed that the proposed system achieves
perfect recognition accuracy (0% ERR) regardless of the key
size. This is achieved at the expense of storing the positionsof the consistent bits in the application database in order tosuccessfully match the probe sample with the gallery sample
during authentication. If the adversary could gain access ofthese position indices, he might try to cross-match them acrossdifferent applications. As a future work, we intend to deal withthis issue via utilizing the challenge-response protocol in orderto recover these indices without explicitly storing them in theapplication database.
IX.

ACKNOWLEDGMENT

We would like to thank the Deanship of Scientific Research
at Jouf University for providing the financial support for
publishing this work.

198
938
260
200

FRR
(%)
0.47
0.8
0.775
0.06650.0562
1.04
0.52
0
3.63

FAR
(%)
0
0
0
0
< 10−50.055
000unlimited00

Key size
140
92
128
42

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