Improved cancelable fingerprint templates using minutiae-based functional transform

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1 SECURITY AND COMMUNICATION NETWORKS Security Comm. Networks 2014; 7: Published online 17 May 2013 in Wiley Online Library (wileyonlinelibrary.com)..788 SPECIAL ISSUE PAPER using minutiae-based functional transform Daesung Moon 1, Jang-Hee Yoo 1 and Mun-Kyu Lee 2 * 1 ETRI, Korea 2 School of Computer and Information Engineering, Inha University, Korea ABSTRACT Since Ratha et al. introduced the functional transform for cancelable fingerprint templates, a few simulation attacks to this method have been proposed. The attacks are based on the fact that the transform depends only on the predefined parameters. That is, the attacker may fully simulate the transform and partially invert it if the parameters are available. Although an original template is transformed using different parameters for different systems, even the compromise of only one of these systems may reveal the original template, which may be a serious potential threat from a practical viewpoint. In this paper, we propose an improved functional transform, whose parameters are decided by the original template, as well as predefined user-specific keys. Because the information on the original template will not be available to the attacker even when a system is compromised, the proposed method significantly improves the security of the original template by preventing the attacker from reconstructing the transform. Copyright 2013 John Wiley & Sons, Ltd. KEYWORDS user authentication; cancelable biometrics; cancelable fingerprint template; functional transform *Correspondence Mun-Kyu Lee, School of Computer and Information Engineering, Inha University, Korea. mklee@inha.ac.kr 1. INTRODUCTION Cancelable biometrics [1,2] is a promising solution to security and privacy concerns about biometrics. The system does not store the original biometric data in this method, but it only stores the templates transformed by a noninvertible transform. Then, authentication is carried out on this transformed biometric data without a need to access the original data. Note that in this setting, a user s biometric data should remain safe, even if the system is compromised. The user just may cancel the old transformed biometric data, and enroll in the system with a new one, by another transform. Since Ratha et al. published their seminal paper on cancelable biometrics [1], various schemes for cancelable biometrics have been introduced. These include several reviewed in [3] and more recent ones, such as Teoh et al. s Biohashing [4], Ang et al. s key-based transform [5], Uludag et al. s fuzzy fingerprint vaults [6], and Lee et al. s local minutiae-based scheme [7]. The best known scheme for cancelable fingerprint templates is Ratha et al. s method [8], which transforms a fingerprint template into a completely different one using predefined parameters. However, it was shown that the original fingerprint template may be recovered if a transformed template, and the parameters used for this transformation are compromised [9,10]. The idea given in [8], that a single template may be transformed into various forms using different parameters, is novel and appropriate, but its implementation should be improved to fully accommodate the idea. The attacks presented in [9,10] use the fact that the knowledge of transformation parameters enables an attacker to partially invert the transform. The problem is that the transformation depends only on the predefined parameters that are revealed to the attacker when the system is compromised. Therefore, we modify the scheme so that a transformation should depend on some information not directly accessible by an attacker even if the system is compromised. An obvious example of this kind of information is the original template. Therefore, we design our transformation method so that both the minutiae positions in the original fingerprint template and the predefined parameters contribute to the shapes of transformation functions. Throughout this paper, a minutia means a fingerprint feature point composed of four elements, that is, x and y coordinates, angle, and type. Therefore, a minutia can be represented by m i =(x i,y i,θ i,t i ). A tricky point in this approach is that only a small Copyright 2013 John Wiley & Sons, Ltd. 1543

2 perturbation in the original template may cause a big change in the transformed one, because the minutiae positions play the two roles at the same time; they are the input to the transformation function, and they decide the function itself. To prevent this amplification of errors, we also propose various compensation techniques. The remainder of this paper is organized as follows. In Section 2, we review Ratha et al. s functional transform [8] and known attacks to it. Section 3 provides our improved functional transform as well as several refinement techniques. The performance of these techniques is evaluated in Section 4. Section 5 concludes the paper with suggestion for future work. 2. REVIEW OF RATHA ET AL. S FUNCTIONAL TRANSFORM Ratha et al. [8] introduced three methods to irreversibly transform a fingerprint template, that is, Cartesian, polar, and surface folding transforms. They mentioned that the third method is the most practical; thus, we concentrate on the surface folding transform, a.k.a. the functional transform, in this paper. Ratha et al. s functional transform is a one-way transform that moves minutiae positions according to the function defined by the predefined parameter set specific to each user. That is, the transform is described as a function over the feature domain that takes as input, the coordinates (x,y) of a possible minutia position and produces as output the amount and the direction of shift at this position. In [8], Gaussian mixtures generated by accumulating twodimensional Gaussian kernels were used for this purpose. The centers and shapes of these Gaussian kernels are parameterized by the unique key associated with each user. To be precise, a Gaussian mixture Fz! ðþfor a position vector z =[x,y] T is defined as follows. First, its magnitude is defined as Fz ðþ¼ X pi! i j2pλij e 1 2 ðz m i Þ T Λ 1 i ðz m Þ i ; where the weight p i, covariance Λ i, and center m i of each Gaussian kernel are parameters given by the key. Then, the phase is n o defined as Φ F ðþ z ¼ 1arg r! 2 Fz ðþ þ Φ rand ; where Φ rand is the phase offset specified by the key. In [8], the authors used two separate functions! F and! G to avoid any form of correlation between the direction and the amount of a shift. Thus, a transformation (x,y,θ)! (X,Y,Y) isdefined by X ¼ x þ K! G ðx; yþ þ KcosðΦ F ðx; yþþ; (1) Y ¼ y þ K! G Y ¼ ðx; y Þ þ KsinðΦ F ðx; yþþ; (2) ðθ þ Φ G ðx; yþ þφ rand Þ mod 2p; (3) where K is a predefined constant. Figure 1 shows an example of a Gaussian mixture produced by placing 24 Gaussian distributions in the image space, where each distribution has a standard deviation of 50 pixels as in [8]. The magnitudes of! F and! G are scaled so that the highest and lowest peaks have values of 1 and 0, respectively. A fingerprint template may be transformed into a completely different one using two Gaussian mixtures, as shown in Figure 2. The rationale for this design is that the function should have a folding effect, that is, there are transformed regions that originate from multiple locations in the original space to satisfy the noninvertibility requirement. It was claimed in [8] that thanks to this noninvertibility property, an attacker cannot recover the original template, even if he or she obtains the transformed template, as well as the parameters, that is, the user-specific keys. However, given K,!! F, and G, an attacker can simulate (1) and (2) to build a Figure 1. Example Gaussian mixture! F generated accumulating 24 two-dimensional Gaussian distributions. magnitude j! Fz ð Þj and phase Φ F ðzþ Security Comm. Networks 2014; 7: John Wiley & Sons, Ltd.

3 Original template Transformed template Figure 2. Transformation of a fingerprint template by surface folding transform based on two Gaussian mixtures. original template and transformed template. dictionary consisting of 4-tuples (x,y,x,y) that has elements. It is possible to find the original minutiae points (x,y,θ) by matching the transformed minutiae points with the dictionary, because the transformed template, that is, the set of 3-tuples (X,Y,Y), is also given to the attacker. Some points are not uniquely recovered because of the many-to-one property of the transform. However, Quan et al. [9] pointed out that most of the regions do not have any overlap with other regions in the transformed template, while they have distortion. We can verify this fact from Figure 5(c) in [8]. According to the experiments in [9] that were performed on FVC2002 DB1 [9], about 90% of the minutiae positions in the transformed template have only one preimage. Moreover, if the attacker may obtain two or more pairs of (key, transformed template) originating from the same original template by attacking multiple databases, then almost 100% of the minutiae positions are uniquely determined [10]. 3. CANCELABLE FINGERPRINT TEMPLATES BASED ON MODIFIED FUNCTIONAL TRANSFORM 3.1. Basic method We observe that the vulnerability of [8] stems from the fact that the transform depends only on the predefined parameters. We improve on Ratha et al. s scheme so that the minutiae points in the original template also contribute to the distribution of Gaussian kernels, thus, the shapes of transformation functions. As a result, the attacker cannot simulate the transformation even if a system is compromised, because the information on the original minutiae points will not be available to the attacker in this case. Our method also requires a key specific to each user as in the original method. However, this key does not directly determine the locations of Gaussian kernels, but it only provides the information to extract the coordinates of Gaussian kernels from the original template. To be precise, the key defines rectangular regions, that is, the positions and dimensions of rectangles, as shown in Figure 3. We use eight rectangles in this example and throughout this paper, although this may be customized for each application. Then, each rectangular region is expanded to fit into the region, and the original minutiae inside the rectangle are moved accordingly, as shown in Figure 4. For example, the marked minutia in the lower left corner in Figure 4 moves to the marked position in Figure 4. Repeating this procedure for the other three rectangles, we finally obtain four expanded images. Then, by overlapping the four images, we obtain a final image with more points, such as Figure 4(c). Each point in this figure serves as a center for a Gaussian distribution with the same magnitude, 1, and the same standard deviation of 50 pixels. In the original transformation [8], each Gaussian distribution has a predefined peak value of either + 1 or 1, a distinct weight p i and covariance Λ i. In our method, we give a peak value according to the type of each minutiae; + 1 for ridge endings and 1 for ridge bifurcations. However, we cannot specify the weight and the covariance for each Gaussian distribution because the minutiae are changed in every trial for fingerprint recognition and any specific Gaussian kernel cannot be named. Therefore, we give the same shape and weight to all distributions. According to our experiments, this change does not affect the overall performance of fingerprint verification. After we generate as many Gaussian distributions as the center positions shown in Figure 4(c), we accumulate these distributions to obtain! F. We repeat the same task for! G. Then, a functional transform is defined by the formulas (1), (2), and (3). For this to be possible, a user-specific key should contain the positions and sizes of eight rectangles, as well as the phase offset Φ rand. We see that it is impossible for an attacker to build a dictionary of (x,y,x,y), because the exact formulas (1), (2), and (3) for transformation cannot be obtained without the knowledge of coordinates of original minutiae. Therefore, the original template remains safe, even if a transformed template and the specifications of rectangles are revealed to the attacker from a compromised database. Security Comm. Networks 2014; 7: John Wiley & Sons, Ltd. 1545

4 Four rectangles to extract Guassian kernels for Four rectangles to extract Guassian kernels for Figure 3. Examples of rectangular regions to extract! F and! G, overlapped with a fingerprint template. four rectangles to extract Gaussian kernels for! F and four rectangles to extract Gaussian kernels for! G Parameter selection We need to be cautious in the selection of the rectangles, because they play a dominant role in our scheme. In fact, Figure 3 contains both good and bad examples of rectangles. We see that the narrow rectangle in the upper part of Figure 3 contains no minutia in the original template; this means that this rectangle does not contribute to the transformation. The same problems exist in another narrow rectangle in Figure 3 and a small rectangle in the upper part of Figure 3. Examining these bad rectangles, we see that the size of a rectangle matters. That is, a small rectangle tends not to contain a sufficient number of minutiae. A straightforward solution to both of these problems is to use only big rectangles. However, using only big rectangles poses another problem. For example, consider an extreme case where all of the four rectangles almost span the entire region. Then, the overlapped image will be almost the same as the original template, which significantly limits the variation in the transform. Therefore, our final choice is to mix large and small rectangles. According to our extensive experiments, the optimal choice is to restrict the length of each side of a rectangle to between 128 and 134 pixels Alignment It is difficult to match two transformed templates, if their original minutiae positions have not been measured with regard to the same coordinate system, because functional transforms do not preserve geometric relations between minutiae. Therefore, it is preferable to pre-align the minutiae positions before transformation. Ratha et al. [8] address this issue using advanced techniques to determine core and delta points [12,13], as well as their own improvements on these techniques [14]. However, even these approaches cannot provide the exact alignment but only guarantee an approximate alignment. According to ([15], p. 236), core point(s) are difficult to reliably extract in a poor quality fingerprint. Moreover, due to variations in placement of the finger on the sensor, the core point may not always be imaged. Note that even a small error in alignment may be amplified to a big change in a transformed template. Especially in our modification, we should be more cautious, because the change in an original minutiae position will be accumulated in an erroneous function, as well as in an erroneous input. Therefore, we apply a more fault-tolerant technique, geometric hashing [16], that stores the minutiae information redundantly. To be precise, our registration phase should be as follows: Obtain a user s fingerprint template. Let T be this template, and let N be the number of feature points in T. Let P 1...P N be the feature points. Make N copies of T. Let T 1...T N be these copies. For i =1 to N, shift and rotate T i so that P i may be located at the center of the image, and its orientation may be in parallel with the X-axis. Apply the functional transform separately to each of the modified templates. Let T T 0 N be the transformed templates. Store T T 0 N as well as the parameters for the transform. On the other hand, the matching phase is as follows: Obtain a user s fingerprint template. Let U be this template, and let M be the number of feature points in U. Note that M may not be the same as N even if the user is the legitimate owner of T. Let Q 1...Q M be the feature points. Make M copies of U, and let U 1...U M be these copies. For j =1 to M, shift and rotate U j so that Q j may be located at the center of the image, and its orientation may be in parallel with the X-axis. Apply the functional transform separately to each of the modified templates by using the parameter 1546 Security Comm. Networks 2014; 7: John Wiley & Sons, Ltd.

5 (c) (d) Figure 4. Extracting centers of Gaussian kernels and generating a transformed template. rectangle expanded to region, Feature points moved according to expansion. Points outside the rectangle are discarded, (c) four expanded regions overlapped to decide the centers of Gaussian kernels used for! F, and (d) repeating the same task for! G and applying the formulas (1), (2), and (3), we obtain the transformed template. associated to the claimed user. Let U U 0 M be the transformed templates. Try to match T 0 i and U 0 j for i =1...N and j =1...M. Find a pair (T 0 i,u 0 j) with the maximum matching score. If this score is above a predefined threshold, accept the user. We remark that in an environment where resources are highly constrained, we may select random subsets of {T 0 i} and {U 0 j} of reasonable sizes instead of using all combinations to speed up the matching process Quantization of minutiae positions In our method, the positions of minutiae decide the centers of Gaussian kernels. Simultaneously, they are input to the transform, which means that a small perturbation in the original template may be amplified through a transform. Figure 5 shows an extreme case of error amplification. This example is one of the pre-aligned templates according to the geometric hashing method explained in the previous section, where A is the reference point. Assume that a small error occurs when an image recognition procedure locates the minutiae B and C, and they are slightly misaligned to D and E, respectively, as in Figure 5. The rectangle is expanded to fit into the entire image, and the resulting image will contain A 0 and D 0 originating from A and D, respectively. (Throughout this paper, X 0 denotes a point in an expanded image that originates from X in the original image.) Then, one of the Gaussian distributions will be centered at D 0, which should be B 0 if correctly aligned. Figure 5 shows an overlapped view of the expanded image and the Security Comm. Networks 2014; 7: John Wiley & Sons, Ltd. 1547

6 Figure 5. Error amplification due to perturbation. minutiae B and C slightly misaligned to D and E, respectively and point E is farther from the center of a Gaussian distribution, D, than it should originally be. original template, where the points from the expanded image, that is, A 0 and D 0 are centers of Gaussian distributions and the points from the original template, that is, A, D, and E, are input to the functional transform. We see that E is quite far from D 0, whereas its correct location, C, should be very close to the correct center, B 0. Thus, the transformed template may be quite a different one from what it should be. We can partially solve this problem by quantizing minutiae positions before extracting Gaussian distributions from them. In our modification, we do not directly use the overlapped image, such as Figure 4(c), to generate Gaussian surfaces. Instead, we partition the overlapped image into square cells of the same sizes, where the size of each cell is a predefined parameter. This parameter may be the same for all users of a system, but it may be distinct to each user. Then for each cell, we check if there is any minutia position in it, and mark all the cells that have minutiae positions. Next, we delete all the points and locate a single point at the center of each marked cell. Even if there are multiple points in a cell, only a single representative point will be given to a single cell. If a point is on the edges of two neighboring cells, it should contribute to only one of them according to a predefined rule, for example, a right and upper cell may have the priority. Thus, the centers of Gaussian kernels are located only on a grid. Figure 6 illustrates the effect of quantization. We see that in this example, even if B 0 and C 0 are inappropriately placed, that is, as D 0 and E 0, respectively, the quantized points will remain correct, as shown in Figure 6. We remark that we also should modify the peak value of each Gaussian kernel to apply this technique. Assume that there are two points in a cell and they are a ridge ending and a ridge bifurcation, whose peak values are + 1 and 1, respectively. Thus, the peak value of the representative point of this cell is not defined. Therefore, we decide to use only one peak value, that is, + 1, for all Gaussian kernels. Partition template into same sized square cells. Points in each cell moved to representative point at the cell center Figure 6. Quantization of minutiae positions. partition template into same sized square cells and points in each cell moved to representative point at the cell center Security Comm. Networks 2014; 7: John Wiley & Sons, Ltd.

7 3.5. Duplicated quantization Quantization of minutiae positions mostly solves the problem of error amplification. However, this approach may cause a side effect in some rare cases. Figure 7 illustrates this situation. Assume that the point B 0 is misplaced into D 0.AsD 0 is in the neighboring cell, the error, that is, the distance between B 0 and D 0, will be increased after quantization to the distance between the centers of two neighboring cells. The same problem occurs for C 0 that is misplaced into E 0. We quantize each point twice to compensate this side effect. However, for the second quantization, the grids are relatively shifted from those for the first quantization. Figure 7 explains this idea. In this figure, the dashed lines generate the cells for the second quantization, whereas the gray solid lines are for the original quantization. Then, we merge the results of two quantizations, which doubles the number of Gaussian kernels. We see that even if B 0 and D 0 belong to two different cells in the original quantization, they belong to the same cell in the second quantization. Figure 7(c) and (d) shows the overall effects of this duplicated quantization. Figure 7(c) is the result of a correct quantization when we apply the duplicated quantization, where B 0 1, C 0 1 are from the original quantization and B 0 2, C 0 2 are from the second quantization. If B 0 and C 0 are incorrectly located into D 0 and E 0,asin Figure 7, the result will be like that in Figure 7(d). Note that B 0 2 = D 0 2 and C 0 2 = E 0 2. Now, we explain why the duplicated quantization approach may alleviate the side effect of quantization. First, we recall that all the points in Figure 7(c) and (d) will serve as the centers of two-dimensional Gaussian distributions. Then, our approach may be summarized as adding a correct Gaussian distribution, for example, centered at B 0 2 = D 0 2, near an incorrect distribution, for example, centered at D 0 1, to reduce the influence of incorrect (c) (d) Figure 7. Effects of duplicated quantization. quantization amplifies errors between B and D and between C and E, each point is quantized one more time to the cell centers generated by dashed lines, (c) quantized points after merging the results of two quantizations from B and C, and (d) quantized points after merging the results of two quantizations from D and E. Security Comm. Networks 2014; 7: John Wiley & Sons, Ltd. 1549

8 distribution. We give an example with one-dimensional Gaussian distributions in Figure 8 to help understand the rationale for this approach. Although this setting differs from that of two-dimensional distributions, the overall tendencies are sufficiently similar. Now, assume that there is only one minutia position x = 1 in our one-dimensional template. Then, G B (x) in Figure 8 represents the correct Gaussian distribution centered at this point. It is scaled so that its maximum value is 1. Assume that an error occurs and it is shifted to x = 1, producing G D (x). Then, the amount of error in functional transformation at a specific position x resulting from this shift can be represented as E 1 (x) =G B (x) G D (x). Now, assume that there are originally two Gaussian distributions G B (x) andg new (x) instead of just one, and G new (x) remains correct when G B (x) is shifted to G D (x). The correct functional transform is expressed as G B (x) +G new (x), because a functional transform is defined by overlapping all the Gaussian distributions. However, a scaling factor c should be multiplied to this function, so that it may have 1 as its maximum value. In this example, c 1/1.76. Figure 8 plots the resulting function, as well as its erroneous version c(g D (x) +G new (x)). We see that each of the merged functions c(g B (x) +G new (x)) and c(g D (x) +G new (x)) seems like an individual Gaussian distribution, and the distance between their peaks is much smaller than that between G B (x) andg D (x). Intuitively, this means the amount of erroneous shift of G B (x) has been reducedbyaddingg new (x). To be precise, we see that the new error function E 2 (x) = c(g B (x) +G new (x)) c(g D (x)+ G new (x)) = c(g B (x) G D (x)) should have a smaller value than the original error function E 1 (x) for all x, asc < 1. Returning to the two-dimensional setting in Figure 7, we see that although the Gaussian surface accumulating the Gaussian kernels centered at D 0 1, D 0 2, E 0 1, E 0 2 still differs from that of Figure 7(c), it significantly reduces the negative effect of D 0 1 and E 0 1. Finally, we remark that this technique can also be applied to alleviate the error amplification problem of the Cartesian and polar transformations in [8]. 4. EXPERIMENTAL RESULTS In this section, we evaluate the performance of our method using FVC2002 DB1 set A [11]. Our experiments were carried out according to the evaluation procedure in the FVC2002 competition. That is, each transformed template is matched against the remaining seven transformed templates of the same finger to compute the false non-match rate, and the first transformed template of each finger is matched against the first transformed templates of the remaining fingers to compute the false match rate. Thus, the numbers of genuine tests and false acceptance tests are 8 7/2 100 = 2800 and /2 = 4950, respectively. We used a Microsoft Windows based machine with a 2.66 GHz CPU and 3 GB RAM, and computed the equal error rate (EER) as a performance indicator. Table I compares the performance of our modified functional transform and various refined versions. The first row shows the EER of the geometric hashing without any functional transform, whereas the second row shows the value for our modified functional transform. As explained in Section 2, we restricted the length of each side of a rectangle to between 128 and 134 pixels. The third and the fourth rows show the EERs for the quantization method (Section 3.4) and the duplicated quantization method (Section 3.5), respectively. We used pixel cells for both quantization methods, because its performance is slightly better than other choices, such as and according to our experiments. We see from Table I that quantization notably improves the performance of our functional transform, and duplication also has a positive effect, although it is minor. In the quantization method, the total execution times of the false non-match rate and false match rate tests are approximately 1.8 and 1.9 s, respectively. Table I. EER comparison for functional transformation methods. Method EER (%) Figure 8. Effect of duplicated quantization in one-dimensional setting. We add a new distribution G new to reduce the error between G B and G D. Error significantly reduced from E 1 to E 2. No transform (geometric hashing) 4.5 Modified functional transform 12.4 Quantization (cell size: 16 pixels) 7.4 Duplicated quantization 7.2 EER, equal error rate Security Comm. Networks 2014; 7: John Wiley & Sons, Ltd.

9 5. CONCLUSION AND FUTURE WORKS In this paper, we presented a new functional transformation method to enhance the security of Ratha et al. s method [8]. In our method, the transformation function depends on the original fingerprint template, as well as predefined parameters, to prevent an attacker from building an attack dictionary, even if the system is compromised. On the basis of our experimental results, we verified that the proposed approach can perform fingerprint verification more securely without a significant degradation of verification accuracy. However, we remark that it would be still necessary to enhance the verification accuracy of our method for its broader acceptance, because our method does not achieve the same performance as biometric verification methods with no transformation. We found out that, in most cases, complete deletion of some part of the fingerprint image may have a negative effect on verification accuracy. A possible solution would be to use a convex hull of two transformed templates, instead of templates themselves for matching. However, a precise analysis should be carried out with this approach, because the convex hull of two transformed templates does not exactly reflect the convex hull of original templates. We leave this issue for our future work. ACKNOWLEDGEMENTS M.-K. Lee was supported by the IT R&D program of MKE/KEIT [ , Intuitive, convenient, and secure HCI-based usable security technologies for mobile authentication and security enhancement in mobile computing environments] and by the MKE/ITRC (Information Technology Research Center) support program (NIPA H ) supervised by the NIPA(National IT Industry Promotion Agency). REFERENCES 1. Ratha NK, Connell JH, Bolle RM. Enhancing security and privacy in biometrics-based authentication system. IBM Systems Journal 2001; 40(3): Lee Y, Lee Y, Chung Y, Moon K. Secure face authentication framework in open networks. ETRI Journal 2010; 32(6): Uludag U, Pankanti S, Prabhakar S, Jain AK. Biometric cryptosystems: issues and challenges. Proceedings of the IEEE 2004; 92(6): Teoh ABJ, Ngo DCL, Goh A. BioHashing: Two factor authentication featuring fingerprint data and tokenised random number. Pattern Recognition 2004; 37(11): Ang R, Safavi-Naini R, McAven L. Cancelable keybased fingerprint templates. In ACISP 2005, Vol of LNCS. Springer: New York, 2005; Uludag U, Pankanti S, Jain AK. Fuzzy vault for fingerprints. In AVBPA 2005, Vol 3546 of LNCS. Springer New York, 2005; Lee C, Choi J-Y, Toh K-A, Lee S, Kim J. Alignmentfree cancelable fingerprint templates based on local minutiae information. IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics 2007; 37(4): Ratha NK, Chikkerur S, Connell JH, Bolle RM. Generating cancelable fingerprint templates. IEEE Transactions on Pattern Analysis and Machine Intelligence 2007; 29(4): Quan F, Fei S, Anni C, Feifei Z. Cracking cancelable fingerprint template of ratha. In 2008 International Symposium on Computer Science and Computational Technology, pages IEEE Computer Society, Shin SW, Lee M-K, Moon D, Moon K. Dictionary attack on functional transform-based cancelable fingerprint templates. ETRI Journal 2009; 31(5): Maio D, Maltoni D, Wayman JL, Jain AK. FVC2002: second fingerprint verification competition. In Int. Conf. Pattern Recognition (ICPR 2002), pages IEEE Computer Society, Nilsson K, Bigun J. Localization of corresponding points in fingerprints by complex filtering. Pattern Recognition Letters 2003; 24(13): Nilsson K. Symmetry filters applied to fingerprints, PhD thesis, Chalmers Univ. of Technology, Sweden. 14. Chikkerur S, Ratha NK. Impact of singular point detection on fingerprint matching performance. In Fourth IEEE Workshop on Automatic Identification Advanced Technologies, pages IEEE Computer Society, Ratha N, Bolle R. Automatic Fingerprint Recognition Systems. Springer: New York, Chung Y, Moon D, Lee S, Jung S, Kim T, Ahn D. Automatic alignment of fingerprint features for fuzzy fingerprint vault. In Information Security and Cryptology, First SKLOIS Conf., Vol of LNCS. Springer: New York, 2005; Security Comm. Networks 2014; 7: John Wiley & Sons, Ltd. 1551