Dual Layer Structure Check (DLSC) Fingerprint ... - CiteSeerX

Dual Layer Structure Check (DLSC) Fingerprint Verification Scheme Designed for Biometric Mobile Template Protection Kai Xi and Jiankun Hu School of Computer Science and IT RMIT University Melbourne, Australia, 3001 {kxi, jiankun}@cs.rmit.edu.au memorize anything. Among various biometric features, fingerprint technology has been most successful in real life applications and is the primary technology used in criminal investigation. The wide usage of fingerprint as forensic application can trace back to 18th century. Currently most fingerprint systems are designed for personal use beyond criminal domain such as e-commerce.

Abstract—Recently biometric fingerprint mobile template protection has attracted research attentions. One major challenge is finding reliable biometric features that are robust against distortion and registration error. It is also desirable that raw fingerprints are not easily recovered once the template has been compromised. In this paper, we propose a new Dual Layer Structure Check (DLSC) fingerprint verification algorithm based on composite features which are reliable, distortion tolerant and registration free. The algorithm shows a great promise for incorporation into existing template protection schemes such as Fingerprint Fuzzy Vault. In addition, the simplicity nature of our scheme makes it easy to be implemented on mobile devices at either software or hardware level. Our experimental results on public domain database FVC 2002 show that the scheme can achieve a low FRR with low FAR.

RNCOS [2] provides an extensive research and professional analysis on the growing biometric security market. It predicts “biometrics technology is being increasingly preferred around the world for access control or personal identification. Further, the global biometrics market is expected to reach a peak of $4.6 billion in 2008, up from $719 million in 2003.” For fingerprint, WinterGreen Research Inc. [4] analyses current equipment market and predicts that “markets at $142.4 million in 2004 are anticipated to continue growing through 2010 when they reach $12.6 billion.”

Index Terms—fingerprint, minutiae matching, biometric template protection, composite feature, mobile

I.

Although fingerprint authentication technology is mature, protecting fingerprint biometric template stored in mobile devices e.g. smartcard is still an emerging research issue and attracts attentions. This is because biometric trait can not be replaced easily if compromised (unlike password and key).

INTRODUCTION

Sensitive information should only be accessed by legal users. With the overall level of fraud steadily rising, access security becomes more and more vital. It is highly desirable to have reliable information security mechanisms to protect sensitive data and personal resources. Traditionally, knowledge based (PIN/password) and token based (key/smartcard) system have been used to verify person’s identity. Users will be granted access when they input correct PIN/password or present genuine key/smartcard. However, these two kinds of systems show crucial weaknesses. Knowledge such as PIN/password can be guessed or broken down via brute force while key and smartcard can be lost, stolen or even replicated.

As a feasible solution of template protection, biometric cryptosystems improve the security and privacy of templates by incorporating these biometric features into a cryptographic framework. One of the typical bio-cryptosystems is Fuzzy Vault, proposed by Juels and Sudan [1]. For fingerprint based system, Fuzzy Vault works with minutiae represented as an unordered set and can overcome the problem of missing or spurious minutiae effectively. Several modified version of fingerprint fuzzy vault schemes have been proposed in [12][13][14]. However, most of these schemes rely strongly on image alignment/registration, a process that is well known to be nontrivial and unreliable. Therefore, inaccurate image registration, combined with variance of minutia coordinates, results in bad system performances (high FRR and FAR) [12][13][14].

As an alternative to password/PIN authentication technology, biometric security technology identifies people by their physical personal traits and therefore inherently requires that the person to be identified is physically present at the point of identification. The physical personal traits can be fingerprint, hand geometry, palm print, face, iris, ear, signature, speech, keystroke dynamics and etc. Biometric techniques can solve many problems in password/ PIN systems: they can confirm that a person is actually present without asking him/her to

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The performance of a bio-cryptosystem is determined by the accuracy of its corresponding biometric feature matching mechanism. In other words, the overall system performance

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should be worse or at most equal to the performance of its underlying matching algorithm. Moreover, the selected biometric feature used in fuzzy vault should be measurable and quantitatable. Minutia local structure based feature and its corresponding matching algorithms are promising for fingerprint fuzzy vault. In [3], it is proposed a minutiae matching algorithm to deal with elastic distortions. The method first compare local minutiae neighborhood structures to find possible matching minutiae pairs. Then conduct a global matching process in order to find registration parameters for fingerprint alignment. The performance of this method is strongly determined by accurate registration.

!i

Fig. 1. (a) Composite feature of Mi (b) Relative location to Singular Point

Suppose that there are two minutiae, a central minutia . Fig. 1(a) depicts the definition of and its neighbor minutia composite features. Here, we express them in a triplet form as , where is the length of connecting ; is the difference between orientation angle of and and , and . is the counter-clockwise and direction from to angle between the orientation of , where .

In [5], the authors proposed a minutiae matching scheme based on both local structure and global features. Local structure, consist of a minutia and its k-nearest neighbor minutiae, is matched for an efficient pre-alignment of test fingerprint and template. Then global minutiae matching strategy will be used for an overall decision. Similar ideas were proposed in [6][7]. For all algorithms mentioned above, it is apparent that the local structure matching is only for alignment purpose or works as an auxiliary filter to reject completely different query fingerprints. The final verification result should be consolidated by global matching. The difficulties of transferring global feature matching from biometric domain to bio-cryptographic domain restrict these algorithms being employed by fingerprint fuzzy vault.

can be represented by its local structure, a Now collection of composite features. The composite feature set of , denoted as , is defined as: minutia

In principle, existing fingerprint fuzzy vault mechanisms rely heavily on the error correction code to rectify the biometric uncertainty. In [15], J. Hu indicated that error correction coding theory can not distinguish either intra-class variance or interclass variance. Hence it does not have a solid theoretic basis for biometric uncertainty correction. Therefore, more emphasis should be laid on investigating reliable biometric features.

is the number of neighbor minutiae around and it where varies when different number of neighbors have been selected. To improve the uniqueness of the composite feature set of a , we involve another feature that represents the minutia relative to the singular point (SP). As shown in location of Fig. 1(b), is defined as the counter-clockwise angle between and direction from to , where the orientation of . During matching process, will be checked first in order to guarantee that the template minutiae and target minutiae are located in same zone. Moreover, by using as a filter, matching speed can be improved significantly. Three rules are proposed as shown below:

In this paper, we propose a new minutiae structure using the composite feature that is proved to be reliable, robust, distortion tolerant, rotation and translation invariant. Based on this local structure, we introduce a new scheme called Dual Layer Structure Check (DLSC) verification algorithm which only verifies local structure without global feature matching process. Performance evaluation of the proposed scheme on public fingerprint database (FVC2002-DB2) demonstrates that a good verification performance has been achieved.

1). Give higher priority to core than delta in terms of singular point selection. 2). When more than one cores appear, the one with the

The rest of the paper is organized as follows. Section 2 presents the proposed composite features, local structure and DLSC algorithm. Section 3 provides performance evaluation. Section 4 is devoted to the conclusions.

II.

PROPOSED SCHEME

!1

A. Composite Feature The concept of composite feature was firstly proposed in [9], which is used for fingerprint image registration. Inspired by this, we proposed a new rotation free and translation-invariant composite feature.

Fig. 2. Corresponding minutiae of different impressions from the same fingerprint

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denotes x

extraction, image binarization and ridge thinning. Hence, we did not adopted minutia type in our algorithm.

3). If there does not exist singular point e.g. arch-type fingerprints or fail to detect SP due to poor quality image, checking of will be bypassed since is non- determinant and just play a role as a filter only.

C. Dual Layer Structure Check (DLSC) Matching Algorithm Minutia structure based matching algorithms have been reported in [5][6][7]. However, among all of these algorithms, besides local structure matching, global feature matching has also been used as a dominant approach. Global matching results determine the final decision directly while local feature matching works a subsidiary approach only.

least will be selected, where coordinate and y coordinate respectively.

and

For the SP detection, the Fingerprint Orientation Model Based on 2D Fourier Expansion (FOMFE) algorithm [16] has been used. However, high accurate SP detection is preferred of SP is but not mandatory. Since location error allowed and SP orientation angle is not considered, coarse SP detection is accepted.

We develop a novel minutiae matching algorithm named Dual Layer Structure Check (DLSC) algorithm which match local structure only. General idea of DLSC algorithm is and not only by comparing their matching two minutiae own composite feature set and but also checking the set of their neighbor minutiae.

A concrete example is demonstrated in Fig. 2. With four , minutia can be represented by neighbor minutiae

With DLSC algorithm, the process of matching two minutiae consists of several steps as shown below. A concrete example (see Figure 4) is used to offer us a straight-forward illustration. Due to the presence of geometrical transformation and intra-class variation, multiple impressions from same finger can not always be exactly the same. By employing composite features, geometrical transformation problem like shift and rotation can be addressed because relative distance and relative angle features are rotation and shift invariant. Additionally, can be set to handle intradifferent tolerance limits of class variation and distortion.

DLSC Algorithm: Step 1: Let be a template minutia (see Fig. 4(a)) and be a query minutia (Fig. 4(c)). Composite features set and are:

B. Discussion In [8][9], the authors employed minutia type (ridge bifurcation and ridge ending) as a feature for matching. A. Malickas et al. claimed that the types of minutiae are unique for every individual [9]. However, based on a large scale experiment, we found minutia type is unstable although correct minutia location can be detected successfully. Fig. 3 demonstrates two enhanced fingerprint impressions originated from the same finger where their corresponding minutiae show different minutia type. The type of left print is bifurcation while the right one is ridge ending. We believe this variant is due to quality of fingerprint images and it should occur during image pre-processing or image enhancement procedure such as ridge

Step 2: Compare

with

if both

and

exist.

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. (b) structure of

Fig. 4. (a) structure of . (d) structure of

Fig. 3. Corresponding minutiae of different impressions from the same fingerprint

and

.

and

. (c) structure of

are from a template while

are from a query fingerprint.

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Otherwise bypass this step and jump to step 3 directly. Percent

conditional matched and Step 4 will be executed. Otherwise, does not match .

and is defined as . If , where is a predefined tolerance limits of , SP relative location checking is successful and then go to Step 3. If , matching is failed and the entire matching procedure will be terminated immediately. The minutiae and will be judged to be unrelated. error of

Step 3: Compare , with , to find matched pairs. To judge how ‘close’ two feature triplets are, three parameters have been adopted: 1) percent error of and , defined as , 2) percent error of

and

, as

3)percent error of

and

, as

After comparing , and , the corresponding matching rates , , can be obtained. Suppose is below , the result that matches obtained from Step 3 will be changed to ‘unmatched’ and we subtract 1 from . Same applies to and . Assume neighbor minutiae fail to pass the hurdle , Final Matching Rate is defined as:

. Predefined tolerance limits of are respectively. Two feature triplets and are considered to be potentially matched if all three percent errors fall within tolerance limits such that:

If , we will make the final decision that matches . The total number of matched minutia points will increase 1.

Then similarity factor

Step 6: Repeat Steps 1 through 5 to find out all matched , query fingerprint will be minutiae. If regarded as ‘Genuine’ and vice versa.

is calculated to select the closest triplet pair, preventing matching. For instance, if two occurrence of 1 : N or query triplets and both satisfy (5) with template triplet , only the one with least f value will be judged as a ‘matched’. and Step 4: Assume Thus Primary Matching Rate

has of

D. Selection of Matching Area and Parameter Settings Different impressions may generate new areas that are outside the template’s area, i.e., non-overlap areas (Fig. 5(a)). This will run the risk of introducing large number of new below minutiae which can reduce the matching rate . To solve this problem, a strategy, shown in Fig. 5(b), is proposed where areas are selected trying to minimize potential non-overlap areas. The DLSC algorithm is then performed on these selected areas. For each template minutiae, the maximal size of the composite feature set is empirically selected as 38 triplets. During matching procedure, each time we extract one 15-minutiae subset from a certain area of the query fingerprint and compare it with the template. Selected areas include central part (around SP), left, right, top, bottom, up-left, up-right, bottom-left, bottom-right, as shown in Fig. 5(b). Once one query fractional part is found to be matched with template, system will report a match and matching process will be completed immediately. On the contrary, matching failed if none query fractional part matches template, i.e. is less than every time.

matched feature triplets. is defined as

where is total number of selected neighbor minutiae of . As the example shown in Figure 4(a) and 4(c), and has 3 matched feature pairs in total, which are , , . has 4 neighbors , , , . Hence:

, where If matching threshold, and

Step 5: Further check structures of neighbor minutiae, so called second layer check. For a conditional matched and , we compare their matched minutiae pair neighbor minutiae, , and , using triplet matching process described in Step 1-4. Take for instance, and will be compared, where

is a predefined will be considered as

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neighbors should pass the 2nd layer test. Then the probability of one genuine minutia passes the test is:

of finding greater than and equal The overall probability to 5 minutiae among 15 minutiae in test fingerprint image can be calculated as

(a) (b) Fig. 5 (a) Non-overlap area of different impressions from the same fingerprint. (b) Select minutiae from different 9 fractional parts of a query fingerprint

The parameters used experimentally set as:

in

our

implementation are , , , , where is the distance between . Singular Point and target minutia, E. Analysis and Mathemetical Proof In all schemes mentioned in [5][6][7], final matching result is jointly determined by local structure matching and global matching. The reason why these methods can not rely solely on local structure is because local structures usually tend to be similar among different fingerprints [6]. In [10], the authors conduct experiments to test a local structure based matching algorithm called ‘5 nearest neighbor based structure match’ and demonstrated the Equal Error Rate (EER) is around 30%, indicting the False Reject Rate (FRR) is 30% at False Acceptance Rate (FAR) is 30% as well. FRR=30% means around 1/3 of local structures of genuine minutiae pairs cannot to be recognized correctly, while FAR=30% means randomly select 10 minutiae pairs from different fingerprints and 3 pairs matched. Apparently, the high EER makes these series of local structure matching algorithms unreliable and can not be solely used for template matching.

Similarly, the probability passes the test is:

of a fake minutia

The overall False Acceptance probability

is:

It is clear that although the comparison results of individual local structures are not very accurate, e.g. EER=20%, overall matching performance can be improved by DLSC algorithm ) dramatically. In theory, 100% Genuine Acceptance Rate ( ) has been achieved as with 0% False Acceptance Rate ( shown above.

III.

Our proposed DLSC matching algorithm addresses this issue by deploying double layer check mechanism. The second layer structure check is the key point of DLSC and its mechanism can be quantified to a Bernoulli Process model. and is considered Each comparison between structure as an independent Bernoulli trial.

EXPERIMENTAL RESULT

We conduct our experiment on a public-domain database FVC2002-DB2, which contains 800 live-scanned fingerprints (100 fingers each give 8 different impressions) in total. All images are captured by an optical sensor with a resolution of 500dpi. Four impressions (impression 1, 2, 7 and 8) of each finger are used for our experiment. Impressions 3, 4, 5 and 6 in this database contain exaggerated displacement and rotation. Consequently, we did not consider these 4 impressions in our experiment at this stage. Commercial fingerprint recognition software Verifinger SDK 5.0 [11] is used for image enhancement and extraction of minutia coordinates and orientation angles. The FOMFE model [16] is used to detect singular point. Among 400 impressions, 4 impressions cannot be processed by Verifinger. They are fingerprint 6_7 (the 7th impression of finger No.6), 75_8, 86_8 and 97_8. Each fingerprint is pair-matched with one another, which results in a

Based on our experiment using DLSC algorithm with the parameters mentioned before, we found: if only 1 layer structure check (DLSC algorithm Step 1-4) is performed, for probability each single minutia, averagely there is of recognition failure (False Reject) and probability of mismatching (False Accept). Assume a test . For each minutia fingerprint has 15 minutiae and we need to check the 1st layer structure once and the 2nd layer , at least 7 structure for 14 times. In case of

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addition, the optimum value of each parameter such as tolerance limits and matching threshold is worth investigating.

100%

All impressions

ACKNOWLEDGMENT The authors would also like to thank Dr. Fengling Han and Professor Panlop Zeephongsekul of RMIT University for their kind supports. The work is financially supported by the ARC (Australia Research Council) Discovery project DP0985838.

False Rejection Rate (%)

Excluding non-overlapped impressions 10%

REFERENCES

1%

[1]

[2] [3]

0% 1%

10% False Acceptance Rate (%)

100%

Fig. 6. Performance (ROC curve) of DLSC scheme [4]

pairs of comparison. Among total are genuine pairs (i.e. the two in a them, are pair are from the same finger) and imposter pairs (i.e. from different finger).

[5]

[6]

The performance of biometric systems can be evaluated by FRR, FAR and a Receiver Operating Characteristics (ROC) curve that plots FAR versus FRR at various matching is selected from 1 to 13. In thresholds. The threshold Fig. 6, pink curve is obtained when using all 396 impressions. We found that 13 out of 588 genuine pairs report none minutiae matched. Among these 13 pairs, 7 pairs are non-overlapped ones with less than 8 corresponding minutiae pairs. The failure of the other 2 pairs is due to low quality of image and appearance of many spurious minutiae. Incorrect singular point detection results in 1 pair matching failure. The failures of the other 3 pairs are brought about by system error. We conduct the experiment again excluding 7 non-overlapped pairs from 588 genuine pairs. This makes sense because Federal Bureau of Investigation (FBI) in the US claims that no two individuals can have more than eight common minutiae. Thus, for conventional minutia matching methods, at least 8 corresponding minutiae should appear otherwise matching will fail definitely. Using 581 genuine pairs, a blue ROC curve is obtained in Fig. 6, indicating that a better performance has been achieved. IV.

[7]

[8] [9] [10]

[11] [12]

[13]

[14]

CONCLUSIONS [15]

In this paper, we first investigated a new minutiae based local structure represented by composite features. Then a new registration free structure-based matching algorithm has been introduced. This proposed DLSC scheme verifies fingerprints using local structure only, without the help of global features. Mathematical proof is provided as well. Our preliminary experimental results on the public domain fingerprint database demonstrate good verification accuracy. This result as well as the algorithmic simplicity makes DLSC be a promising algorithm that can easily be implemented at small, resourceconstrained mobile devices such as smartcard, mobile phones, pagers, and PDAs for purpose of template protection.

G. A. Juels and M. Sudan, “A Fuzzy Vault Scheme,” in Proceedings of IEEE International Symposium on Information Theory, Lausanne, Switzerland, 2002, p. 408. RNCOS, “World Biometric Market Outlook (2005-2008),” Bazen, A.M., and S.H. Gerez (2002). Thin-plate spline modelling of elastic deformations in fingerprints. In Proc. 3rd IEEE Benelux Signal Processing Symposium (SPS-2002). Leuven, Belgium. WinterGreen Research, Inc. “Biometric Fingerprint Equipment Market Opportunities, Strategies, and Forecasts, 2005 to 2010,”, Pages: 345 X. Jiang, W. Yau. Fingerprint minutiae matching based on the local and global structures, In: Sanfeliu A, Villanueva JJ, Vanrell M, Alquezar R, Crowley J, Shirai Y. ed., Proc. of the 15th Int’l Conf. on Pattern Recognition, Washington: IEEE Computer Society, 2000. 1038-1041. W. Zhang and Y. Wang, "Core-based structure matching algorithm of fingerprint verification", Proc. of ICPR 2002, Volume 1, IEEE, pp 70 74, 11-15 Aug, 2002. A. Kisel, A. Kochetkov, and J. Kranauskas "Fingerprint Minutiae Matching without Global Alignment Using Local Structures" Informatica Volume 19 , Issue 1 (January 2008) Pages 31-44 D.P.Mital and E.K.Teoh "An automated matching technique for fingerprint identification", Proc. of KES 1997, Vol 1, pages 142 - 147 A. Malickas and R. Vitkus, “Fingerprint Registration Using Composite Features Consensus”, Informatica, vol. 10, no. 4, pp. 389-402, 1999. Jeffers, J.; Arakala, A., "Minutiae-Based Structures for A Fuzzy Vault," Biometric Consortium Conference, 2006 Biometrics Symposium: Special Session on Research at the , vol., no., pp.1-6, Sept. 19 2006-Aug. 21 2006 VERIFIER. Neurotechnologija Ltd. Http://www.neurotechnologija.com S. Yang and I. Verbauwhede, “Automatic Secure Fingerprint Verification System Based on Fuzzy Vault Scheme,” in Proceedings of IEEE ICASSP, vol. 5, Philadelphia, USA, March 2005, pp. 609–612. U. Uludag and A. K. Jain, “Securing Fingerprint Template: Fuzzy Vault With Helper Data,” in Proceedings of CVPR Workshop on Privacy Research In Vision, New York, USA, June 2006, p. 163. K. Nandakumar, A. K. Jain, and S. Pankanti, “Fingerprint-based Fuzzy Vault: Implementation and Performance,” IEEE Trans. on Info. Forensics and Security, vol. 2, no. 4, pp. 744–757, December 2007. J. Hu, “Mobile fingerprint template protection: Progress and open issues” The 3rd IEEE Conference on Industrial Electronics and Applications, June 2008.

Further work would include exploring how to modify DLSC algorithm to suit fingerprint fuzzy vault system. In

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