Cancelable Template Research Articles

Biometrics Based Privacy‐Preserving Authentication and Mobile Template Protection

Smart mobile devices are playing a more and more important role in our daily life. Cancelable biometrics is a promising mechanism to provide authentication to mobile devices and protect biometric templates by applying a noninvertible transformation to raw biometric data. However, the negative effect of nonlinear distortion will usually degrade the matching performance significantly, which is a nontrivial factor when designing a cancelable template. Moreover, the attacks via record multiplicity (ARM) present a threat to the existing cancelable biometrics, which is still a challenging open issue. To address these problems, in this paper, we propose a new cancelable fingerprint template which can not only mitigate the negative effect of nonlinear distortion by combining multiple feature sets, but also defeat the ARM attack through a proposed feature decorrelation algorithm. Our work is a new contribution to the design of cancelable biometrics with a concrete method against the ARM attack. Experimental results on public databases and security analysis show the validity of the proposed cancelable template.

Random Projection-based Cancelable Template Generation for Sparsely Distributed Biometric Patterns

Cancelable biometrics, a template transformation approach, attempts to provide robustness for authentication services based on biometrics. Several biometric template protection techniques represent the biometric information in binary form as it provides benefits in matching and storage. In this context, it becomes clear that often such transformed binary representations can be easily compromised and breached. In this paper, we propose an efficient non-invertible template transformation approach using random projection technique and Discrete Fourier transformation to shield the binary biometric representations. The cancelable fingerprint templates designed by the proposed technique meets the requirements of revocability, diversity, non-invertibility and performance. The matching performance of the cancelable fingerprint templates generated using proposed technique, have improved when compared with the state-of-art methods.

Constructing cancellable template with synthetic minutiae

Security measures have to be taken to protect the privacy of biometric data. Cancellablebiometrics is proposed as an effective mechanism of using and protectingbiometrics. The authors propose a new approach of constructing cancellablefingerprint template by mapping real minutiae to randomly generated minutiae ina synthetic template. The synthetic minutiae are selected based on thek-nearest neighbour method. One synthetic neighbour of eachreal minutia is utilised to construct the verification template (VT). Since thesynthetic template is determined by a user-specific PIN and a random salt, theproposed method is in fact a two-factor authentication scheme. Multiple VTs canbe generated easily by applying different PINs and salts to a real template. Toprove the validity of the scheme, testing is carried out on three databases. Afew factors affecting matching are also investigated. The results show that theconstructed templates satisfy the requirements of cancellable biometrics. Falsenon-match rate and False matching rate with the transformed templates can bemuch lower than those with the original templates by properly selecting the sizeof the synthetic templates and the ordinal number of the nearest neighbours. Theproposed approach can also be utilised for multi-generation templatetransformation.

Toward Constructing Cancellable Templates using K-Nearest Neighbour Method

The privacy of biometric data needs to be protected. Cancellable biometrics is proposed as an effective mechanism of protecting biometric data. In this paper a novel scheme of constructing cancellable fingerprint minutiae template is proposed. Specifically, each real minutia point from an original template is mapped to a neighbouring fake minutia in a user-specific randomly generated synthetic template using the k-nearest neighbour method. The recognition template is constructed by collecting the neighbouring fake minutiae of the real minutiae. This scheme has two advantages: (1) An attacker needs to capture both the original template and the synthetic template in order to construct the recognition template; (2) A compromised recognition template can be cancelled easily by replacing the synthetic template. Single-neighboured experiments of self-matching, nonself-matching, and imposter matching are carried out on three databases: DB1B from FVC00, DB1B from FVC02, and DB1 from FVC04. Double-neighboured tests are also conducted for DB1B from FVC02. The results show that the constructed recognition templates can perform more accurately than the original templates and it is feasible to construct cancellable fingerprint templates with the proposed approach.

An Improved Cancelable Fingerprint Template Encryption System Research

For the existing security problem based on Fuzzy Vault algorithm, we propose a cancelable fingerprint template encryption scheme in this paper. The main idea is to firstly construct an irreversible transformation function, and then apply the function to transform the original template and template information is stored after conversion. Experimental results show it effectively prevents the attack from fingerprint template data and improves security of the system by using minutiae descriptor to encrypt abscissa of the vault. The experiment uses public FVC2004 fingerprint database to test, result shows that although the recognition rate of the proposed algorithm is slightly lower than the original program, but the improved algorithm security and complexity are better, and therefore the proposed algorithm is feasible in general.

Securing fingerprint templates using fused structures

This study presents a protection method for fingerprint templates by using fused structures at the feature level. The authors compute two transformed features from minutiae points: namely, local structure and distant structure. These structures are represented as bit-strings. A fusion on bit-strings is done at the feature level to produce a cancelable template. An equal error rate (EER) of 2.19, 1.6 and 6.14% on Fingerprint Verification Competition (FVC) 2002 Database (DB)1 through DB3 databases and an EER of 11.89, 12.71, 17.6% on FVC 2004 DB1 through DB3 proves the tenability of the proposed method.

Design of Alignment-Free Cancelable Fingerprint Templates with Zoned Minutia Pairs

Cancelable fingerprint templates effectively protect original fingerprint data by revoking a compromised template and reissuing a new one. Alignment-free cancelable templates require no image pre-alignment and therefore do not suffer from inaccurate singular point detection. In this paper we propose to construct local minutia structures through zoned minutia pairs in the design of alignment-free cancelable fingerprint templates. Because the structures formed by zoned minutia pairs are truly local, they are more discriminating, which benefits fingerprint matching, leading to good recognition performance. We also apply a simple yet effective measure to mitigating the risk of the attack via record multiplicity (ARM). The proposed method features a partial discrete Fourier transform-based non-invertible transformation, which is compact and computationally efficient. The new cancelable templates meet the requirements of diversity, revocability, non-invertibility and performance. Evaluated over four public databases, the proposed alignment-free cancelable fingerprint templates exhibit superior performance with the Equal Error Rate in the lost-token scenario being 0.19% for FVC2002 DB1, 1% for FVC2002 DB2, 4.29% for FVC2002 DB3 and 9.01% for FVC2004 DB2.

Fingerprint bio-Crypto key generation using Scale Invariant Feature Transform (SIFT)

Network security has become a great threat to the network accessible resources that consists of policies to prevent, monitor unauthorized access, modification, and misuse of computer network. Several algorithms and techniques were proposed for the secure transmission of data and to protect user‟s privacy. Secret-key cryptography and public-key cryptography are the techniques used for the protection of security issues. However, such a key needs to be stored in a protected place or it should be transported by a shared communication line. So generation of cryptographic key using biometric traits of both sender and receiver during communication avoids key storing and improves security strength. The proposed approach for detecting the quality of fingerprint by using the method called orientation certainty level (OCL). If the image has good quality then feature extraction will be done using Scale Invariant Feature Transform, otherwise poor quality image will get ignored. By using cover image the obtained cancellable template will get hidden. Then the hidden image will be transmitted from sender to receiver and receiver to receiver to sender by using Variable Least Significant Bit techniques. Finally the performance metrics like FAR (False Acceptance Rate), FRR (False Rejection Rate), and Accuracy of the proposed work is compared with the existing system.

A privacy-preserving cancelable iris template generation scheme using decimal encoding and look-up table mapping

Biometric-based recognition systems have overcome passive issues of traditional human authentication systems. However, security theft and privacy invasion are two passive issues that still persist in the effective deployment of biometric-based authentication systems. Compromise of biometric data can potentially lead to serious security violation as the user's biometric trait cannot be changed. In order to prevent the invasion of biometric templates, it is desired to morph the original biometric template through non-invertible or irreversible transformation function. This transformed template is referred to as cancelable template and can be replaced or reissued in case of compromise. In this paper, we propose a novel cancelable iris template generation technique based on randomized look-up table mapping. The technique utilizes a decimal vector generated from a rotation-invariant feature vector. The feature vector is generated using 1-D Log Gabor filter applied to the iris image. Experiments carried out on various iris databases confirm the efficacy of the proposed approach. After applying the template protection mechanism, we have achieved Equal Error Rate (EER) of 0.37%, 0.43% and 0.79% for CASIA-V 1.0, CASIA-V3-Interval and ICE 2005 iris databases, respectively. Moreover, the transformation preserves the irreversibility, revocability and diversity properties of the concealable iris templates.

Robust cancellable biometrics scheme based on neural networks

Several cancellable biometrics (CBs) techniques have been proposed to protect biometric data and maintain users' privacy. Although such techniques can withstand brute-force and/or pre-image attacks, they are vulnerable to correlation attacks. In this study, the authors propose a novel correlation attack-resistant CBs scheme that is based on a convolution operation and a bidirectional associative memory (BAM) neural network. The proposed scheme utilises BAM to bind biometric templates to random bit-strings in a secure and efficient manner. These random bit-strings are then employed to derive cancellable templates from the true templates linked to them via BAM weights, which are safely stored with the generated cancellable template in the system database. In this study, linear convolution is adopted as the cancellable transformation process. The result of convolving the original biometric template with the transformation key is binarised according to a predefined threshold to thwart blind de-convolution. The security of the proposed scheme against different attacks is analysed and experiments on the CASIA-IrisV3-Interval dataset illustrate the efficacy of the proposed scheme.

A partial Hadamard transform approach to the design of cancelable fingerprint templates containing binary biometric representations

As an important biometric template protection technique, cancelable biometrics provides enhanced security for biometric-based authentication. Representing biometric information in binary form is useful in biometric template protection because it offers simplicity in feature representation and provides benefits in matching and storage. Therefore, it is no surprise that cancelable biometrics often exploits binary biometric representations as resultant templates. However, very often such binary biometric representations contained in cancelable biometrics have security shortfalls, because once they are compromised, the biometric features that they represent are likely to be retrieved. In this paper we propose an efficient non-invertible transformation – the partial Hadamard transform to securely protect binary biometric representations in the design of cancelable biometrics. When this non-invertible transform is applied to the DFT of the binary biometric representations, the resultant templates are complex vectors, from which the original binary vectors cannot be restored. A prominent feature of the proposed approach is that it preserves the stochastic distance between binary vectors after the transformation. This stochastic distance preserving property is theoretically proved and further demonstrated and verified by experiments. The cancelable fingerprint templates designed by the proposed partial Hadamard transform meet the requirements of revocability, diversity, non-invertibility and performance. Matching performance of the designed cancelable templates compares favorably with state-of-the-art methods that also involve binary biometric representations; for example, the Equal Error Rate of the proposed method in the lost-token scenario is 1% for FVC2002 DB1, 2% for FVC2002 DB2 and 5.2% for FVC2002 DB3.

Generating cancellable fingerprint templates based on Delaunay triangle feature set construction

In this study, the authors propose a novel fingerprint template protection scheme that is developed using Delaunay triangulation net constructed from the fingerprint minutiae. The authors propose two methods namely FS_INCIR and FS_AVGLO to construct a feature set from the Delaunay triangles. The feature set computed is quantised and mapped to a 3D array to produce fixed length 1D bit string. This bit string is applied with a DFT to generate a complex vector. Finally, the complex vector is multiplied by user's key to generate a cancellable template. The proposed computation of feature set maintained a good balance between security and performance. These methods are tested on FVC 2002 and FVC 2004 databases and the experimental results show satisfactory performance. Further, the authors analysed the four requirements namely diversity, revocability, irreversibility and accuracy for protecting biometric templates. Thus, the feasibility of proposed scheme is depicted.

A blind system identification approach to cancelable fingerprint templates

Cancelable biometrics is an important biometric template protection scheme. With no a priori image pre-alignment, alignment-free cancelable fingerprint templates do not suffer from inaccurate singular point detection. In this paper, we develop a blind system identification approach to the design of alignment-free cancelable fingerprint templates. The binary string, derived from quantized pair-minutiae vectors, is to be secured and its frequency samples act as the input to the proposed algorithm. Motivated by the identifiability of the source signal in blind system identification, we propose to protect the binary string׳s frequency samples, which are treated as the source input, by countering or dissatisfying the identifiability condition so that they cannot be recovered from the output complex vector (transformed template). The proposed transform is irreversible because when the identifiability condition is not met, non-identification of the source input is theoretically guaranteed in blind system identification. Security, matching performance and resource requirement are the main factors in cancelable template design. With the size of the transform parameter key and transformed templates being moderate, the proposed method suits resource-limited applications, e.g., smartcards, driver license and mobile phones. Evaluation of the proposed method over FVC2002 DB1, DB2 and DB3 shows that the new method exhibits favorable performance compared to state-of-the-art alignment-free cancelable fingerprint templates.

Multi-algorithmic fingerprint model for cancellable template generation

In this paper we present a multi-algorithmic biometric system comprising the fingerprint biometrics to tackle highly uncertain databases. Two methods were proposed namely, spiral-based method and block-based method to generate cancellable fingerprint templates. Spiral-based method can be used to exploit elements in each contiguous right angled triangle, that are drawn using the invariant distances between reference minutia to every other minutia. In the block-based method, fingerprint image can be tessellated into blocks to get a set of features by considering Euclidean distance from each minutia in reference block to other minutia in blocks which are horizontal, vertical, and diagonal in direction to reference block, and orientation of minutiae. Two biometric methods are fused at score level based on Yager's triangular norm. The experimental evaluations and analysis are performed using the FVC2002 databases. The results show better performance and separation between genuine and imposter distributions.

Multi-algorithmic fingerprint model for cancellable template generation

Multi-algorithmic fingerprint model for cancellable template generation

Kernel PCA enabled bit-string representation for minutiae-based cancellable fingerprint template

Minutiae set is one of the prevalent features used to represent a fingerprint. Many minutiae protection schemes have been proposed in recent literature, but only a few have demonstrated successful conversion from minutiae set to fixed-length bit-string. In this paper, we develop a fixed-length binary cancellable fingerprint template generation scheme based on a minutia descriptor known as the multi-line code (MLC). While retaining the core of MLC algorithm, we transform the unordered and variable-size MLC template into an ordered and fixed-length bit-string using kernel principal components analysis (KPCA) and state-of-the-art binarization techniques. The construction of a proper kernel suited for the scenario was validated using Mercer׳s Theorem. Evaluation of the proposed scheme was performed over several FVC datasets and the best equal-error rate (EER) obtained for the final bit-string is 1.61%. In addition, extensive analysis was done to justify the non-invertibility and revocability property of the cancellable template.

On application of bloom filters to iris biometrics

In this study, the application of adaptive Bloom filters to binary iris biometric feature vectors, that is, iris-codes, is proposed. Bloom filters, which have been established as a powerful tool in various fields of computer science, are applied in order to transform iris-codes to a rotation-invariant feature representation. Properties of the proposed Bloom filter-based transform concurrently enable (i) biometric template protection, (ii) compression of biometric data and (iii) acceleration of biometric identification, whereas at the same time no significant degradation of biometric performance is observed. According to these fields of application, detailed investigations are presented. Experiments are conducted on the CASIA-v3 iris database for different feature extraction algorithms. Confirming the soundness of the proposed approach, the application of adaptive Bloom filters achieves rotation-invariant cancellable templates maintaining biometric performance, a compression of templates down to 20-40% of original size and a reduction of bit-comparisons to less than 5% leading to a substantial speed-up of the biometric system in identification mode.

MINIMUM SPANNING TREE (MST) BASED TECHNIQUES FOR GENERATION OF CANCELABLE FINGERPRINT TEMPLATES

The biometric community is faced with the difficult problem of protection of the original biometric template. One way of doing this is using a cancelable biometric method, which transforms original biometric template in a noninvertible way and uses the transformed template to verify a person's identity. In this paper, we propose two novel representation methods for fingerprint minutiae. Proposed methods based on this representation are simple to generate cancelable templates without requiring pre-alignment of the fingerprints. The main idea is to generate a minimal spanning tree (MST) for fingerprint minutiae in a three-dimensional (3D) feature space. The chain code representation for a generated MST in a two-dimensional (2D) feature space is proposed. A bit string is then generated by mapping the chain code into 2D array. The fingerprint minutiae based upon the Cartesian system is dealt with in Method-1, while the boundary representation of the minutiae is dealt with in Method-2. The proposed methods are evaluated using FVC2004 and FVC2002 databases and the performance is better compared to existing methods [C. Lee and J. Kim, J. Netw. Comput. Appl.33(3) (2010) 236–246; S. Wang and J. Hu, Pattern Recogn.45 (2012) 4129–4137; S. Wang and J. Hu, Pattern Recogn.47(3) (2014) 1321–1329; Z. Jin, A. B. J. Teoh, T. S. Ong and C. Tee, Expert Syst. Appl.39 (2012) 6157–6167; P. Das, K. Karthik and B. C. Garai, Pattern Recogn.45(9) (2012) 3373–3388; A. B. J. Teoh, D. C. L. Ngo and A. Goh, Pattern Recogn.37(11) (2004) 2245–2255.]

Non-Invertible Online Signature Biometric Template Protection via Shuffling and Trigonometry Transformation

This paper describes a novel approach to a cancelable template protection scheme that secures online handwritten signature samples in the reference database of a biometric verification system. We propose a renewable-noninvertible transformation process named Bio-Trigono comprising two consecutive steps. First, a shuffling scheme is applied to a signature sample to attain the renewability property for template protection. This is followed by the deployment of a cosine function for which its periodic characteristic is exploited to achieve a much desired non-invertible property for additional security. The overall template protection scheme was tested rigorously on signature samples of a SIGMA database through an online signature verification system. Its verification utilized the Principal Component Analysis (PCA) for features' extraction and the Artificial Neural Network (ANN) for user reference modeling and classification processes. Results demonstrated an effective cancelable template protection scheme whereby the best averaged error rates were 10. 3, 10. 5 and 14. 1% for un-transformed first and second transformed signature templates, respectively.

Fingerprint template protection using multiline neighboring relation

Security and privacy concern of biometric information leads to the attention for generating cancellable template from the original biometric template. We proposed an alignment free cancellable template generation by construction of M rectangles with different orientations around every reference minutiae followed by the calculation of rotation invariant and translation invariant neighboring relation, plane based quantization for bit string generation and matching. The proposed method fulfills non-invertibility, accuracy, diversity and revocability properties which are the primary requirements of cancellable template design. The EER obtained for FVC 2002 DB1, DB2, and DB3 is 0.62%, 1.33%, and 2.64% for same key scenario. The proposed method shows the better performance for FVC 2002 DB1, DB2, and DB3 compared to the existing methods (Wang & Hu, 2012, 2014; Ahmad et al., 2011).