Mixing Binary Face and Fingerprint based on Extended Feature Vector (EFV) Hashing

The current cryptographic algorithms are suffering from the drawback of maintaining the secrecy of private keys. These private keys must be taken care of so that the cost of privacy can be avoided. Stored private keys are generally secured by the user tailored passwords, which can be hacked by brute-force attacks. Also, it is quite a horrid issue for the user to play with those large keys. So, the major issue is to protect and manage such a long private key. The generation of cryptographic key from individual user's biometric feature is a viable option. In this way it is too difficult for the attacker to guess the cryptographic key without having access of the user's biometrics. But the problem is that once it gets compromised it goes to waste. To solve the aforesaid issues cancelable biometrics approach has been proposed which is a wonderful solution for cancelling and reissuing the biometrics template. In this paper, a way to generate cryptographic key from the cancelable fingerprint template of the user has been suggested. Shuffling based transformation method is applied on the minutia points extracted from the user's fingerprint to generate cancelable template. Then a pair of private and public key is generated for RSA cryptosystems from cancelable template. This cancelable template maintains the secrecy of fingerprint template and produce revocable key for asymmetric cryptography.

Biometric template protection is one of the important issues in deploying a practical biometric system. To tackle this problem, many algorithms have been reported in recent years, most of them being applicable to fingerprint biometric. Since the content and representation of fingerprint template is different from templates of other modalities such as face, the fingerprint template protection algorithms cannot be directly applied to face template. Moreover, we believe that no single template protection method is capable of satisfying the diversity, revocability, security and performance requirements. We propose a three-step cancelable framework which is a hybrid approach for face template protection. This hybrid algorithm is based on the random projection, class distribution preserving transform and hash function. Two publicly available face databases, namely FERET and CMU-PIE, are used for evaluating the template protection scheme. Experimental results show that the proposed method maintains good template discriminability, resulting in good recognition performance. A comparison with the recently developed random multispace quantization (RMQ) biohashing algorithm shows that our method outperforms the RMQ algorithm.

When biometric authentication is performed on On-Body Wearable Wireless Networks, a cancelable template is useful to protect biometric information. A cancelable template generation method converts the original biometric information into irreversibly transformed information to protect the original biometric information. If a cancelable template is damaged or leaked, it can be replaced with another cancelable template. In 2017, Dwivedi et al. proposed a novel cancelable iris template generation scheme based on the randomized look-up table mapping. So far their scheme is the most accurate scheme with respect to EER compared to the previous cancelable iris template generation schemes. However, their scheme is not alignment-free and so is not efficient enough for wearable sensors. In the paper, we first suggest how to improve the accuracy of the Dwivedi et al.’s scheme using the partial sort technique. Our experiment result shows that our suggested scheme is more accurate than the Dwivedi et al.’s scheme under almost all parameter settings. More concretely, our scheme achieves EER 0.09%, whereas the Dwivedi et al.’s scheme achieves EER 0.43% in the best parameter settings for the CASIA-V3-Interval iris database. We also suggest how to improve the efficiency of the Dwivedi et al.’s scheme. Our second scheme is alignment-free by processing IrisCode column-wise, whereas the Dwivedi et al.’s scheme handles IrisCode row-wise. Our experiment shows that our second scheme is 15 times faster than the Dwivedi et al.’s scheme, so our scheme is efficient enough for wearable sensors. Though our second scheme has very high EER under some parameter settings, our second scheme achieves EER 0.53% in the best parameter settings for the CASIA-V3-Interval iris database.

Since the emergence of biometrics and its use in terms of authentication systems, the risks of fraud and identity theft keep increasing steadily. Hence, biometric template protection has become a real challenge for the research community. So several schemes have been proposed to address these issues and minimize the risk of attacks, but it turns out that just few of them have managed to satisfy both performance and security. In this context, we propose in our turn a fingerprint template protection, which consists of exploiting the minutiae structure that shapes the fingerprint, namely their positions and orientations, to then generate cancellable templates using a one-way transformation function. We proved that the proposal perfectly meets the properties of revocability, diversity, as well as accuracy performance. Promising results were obtained during an evaluation on FVC2002 DB1 database and which justifies the feasibility of our technique.

In this paper, a proposed cancellable biometric scheme is based on multiple biometric image identifiers, Arnold’s cat map and double random phase encoding (DRPE) to obtain cancellable biometric templates. The proposed segmentation scheme that is used to select the region of interest for generating cancelable templates is based on chaos entropy low correlation statistical metrics. The objective of segmentation is to reduce the computational cost and reliability of template creation. The left and right biometric (iris, fingerprint, palm print and face) are divided into non-overlapping blocks of the same dimensions. To define the region of interest (ROI), we select the block with the highest entropy. To shorten the registration process time and achieve a high level of security, we select 25% of the image volume of the biometric data. In addition, the low-cost security requirement lies in the use of selective encryption (SE) technology. The step of selecting the maximum entropy is executed on all biometric blocks. The maximum right and left multi-biometric blocks are arranged in descending order from the entropy perspective and select 50% of each biometric couple and store the single matrix. The obtained matrix is scrambled with a certain number of iterations using Arnold’s Cat Map (ACM).The obtained scrambled matrix is encrypted with the DRPE to generate the cancellable biometric templates, which are further concatenated. The simulation results display better performance of the suggested cancellable biometric system in noise scenarios using the area under the receiver operating characteristic (AROC). The strength of the suggested technique is examined with correlation, irregular deviation, maximum difference and maximum deviation. The recommended proposed approach shows that the ability to distinguish the authentic and imposter biometrics of user seven in different levels of the noise environment.

This paper proposes a new, privacy-preserving fingerprint construct, which consists of a single pattern created from a small subset of minutiae from the corresponding minutiae template. The sparsity of the resulting feature vector ensures that it cannot be used to reconstruct the underlying fingerprint image, while simultaneously allowing for the cancellability of compromised patterns. The proposed construct is, therefore, inherently a non-invertible cancellable fingerprint template protection scheme. Experimental results on the recognition accuracy of patterns consisting of three, four, and five minutiae were very encouraging: an FRR of 0.4% or less was achieved for all three pattern sizes, while the FAR ranged from 2.1% for 5-minutia patterns to 6.5% for 3-minutia patterns. It took about 0.1 seconds to verify a single person, regardless of the pattern size. These results suggest that the proposed fingerprint construct is suitable for deployment in fingerprint-based civilian authentication applications.

Cancellable fingerprint template has increasingly received interest in research thanks to not only the security for the user's original features but also the stable performance for the system. In this paper, we propose a new method to design cancellable fingerprint template with local structure by clustering the minutiae using the k Nearest Neighbor (kNN) algorithm. In other words, $k$ minutiae in a fingerprint that are closest to a reference minutia form a local structure. Pairwise features from the reference minutia and each of the member in the cluster are extracted and used for local structure matching. The partial Discrete Fourier Transformation was applied as the non-invertible transformation. This method has been evaluated with four public databases FVC2002 DB1-DB3, and FVC2004 DB2. The Equal Error Rate achieved for each database is 0.2%, 0.04%, 4.78%, and 7.64%, respectively.

In a biometric authentication system using protected templates, a pseudonymous identifier is the part of a protected template that can be directly compared. Each compared pair of pseudonymous identifiers results in a decision testing whether both identifiers are derived from the same biometric characteristic. Compared to an unprotected system, most existing biometric template protection methods cause to a certain extent degradation in biometric performance. Fusion is therefore a promising way to enhance the biometric performance in template-protected biometric systems. Compared to feature level fusion and score level fusion, decision level fusion has not only the least fusion complexity, but also the maximum interoperability across different biometric features, template protection and recognition algorithms, templates formats, and comparison score rules. However, performance improvement via decision level fusion is not obvious. It is influenced by both the dependency and the performance gap among the conducted tests for fusion. We investigate in this paper several fusion scenarios (multi-sample, multi-instance, multi-sensor, multi-algorithm, and their combinations) on the binary decision level, and evaluate their biometric performance and fusion efficiency on a multi-sensor fingerprint database with 71,994 samples.

The need for fool proof authentication procedures away from traditional authentication mechanisms like passwords, security PINS has led to the advent of biometric authentication in information systems. Biometric data extracted from physiological features of a person including but not limited to fingerprints, palm prints, face or retina for purpose of verification & identification is saved as biometric templates. The inception of biometrics in access control systems has not been without its own hitches & like other systems it has its fair share of challenges. Biometric fingerprints being the most mature of all biometric spheres are the most widely adopted biometric authentication systems. Biometric systems effectiveness lies on how secure they are at preventing inadvertent disclosure of biometric templates in an information system’s archive. This however has not been the case as biometric templates have been fraudulently accessed to gain unauthorized access in identification and verification systems. In order to achieve strong and secure biometric systems, biometric systems developers need to build biometric systems that properly secure biometric templates. Several biometric template protection schemes and approaches have been proposed and used to safeguard stored biometric templates. Despite there being various biometric template protection schemes and approaches in existence, none of them has provided the most authentic, reliable, efficient and deterrent means to totally secure biometric fingerprint templates. This research sought to establish status of the current biometric template protection techniques and methods by conducting a survey and analyzing data gathered from a sample of seventy-eight (78) respondents. We will report these results and give our conclusion based on findings of the survey in this paper.

A cancelable biometric identification scheme based on bloom filter and format-preserving encryption

Biometric information security is a relatively new research interest in the field of biometric authentication. In view of the special relationship of human face and human ear in physiological position, the fusion of face and ear is a natural form of multimodal authentication system used for non-intrusive personal identification. This paper proposes a template protection method for multimodal biometric template. The first step is to apply feature level fusion after feature extraction using principal component analysis. The second step converts the face and ear fused real-valued template to binary template using non-invertible transformation. The third step encrypts the binary template by means of fuzzy commitment. The authentication experimental results on a face and ear multimodal dataset show that multimodal biometric template encryption performs better than the unimodal biometric encryption.

To replace compromised biometric templates, cancelable biometrics has recently been introduced. The concept is to transform a biometric signal or feature into a new one for enrollment and matching. For making cancelable fingerprint templates, previous approaches used either the relative position of a minutia to a core point or the absolute position of a minutia in a given fingerprint image. Thus, a query fingerprint is required to be accurately aligned to the enrolled fingerprint in order to obtain identically transformed minutiae. In this paper, we propose a new method for making cancelable fingerprint templates that do not require alignment. For each minutia, a rotation and translation invariant value is computed from the orientation information of neighboring local regions around the minutia. The invariant value is used as the input to two changing functions that output two values for the translational and rotational movements of the original minutia, respectively, in the cancelable template. When a template is compromised, it is replaced by a new one generated by different changing functions. Our approach preserves the original geometric relationships (translation and rotation) between the enrolled and query templates after they are transformed. Therefore, the transformed templates can be used to verify a person without requiring alignment of the input fingerprint images. In our experiments, we evaluated the proposed method in terms of two criteria: performance and changeability. When evaluating the performance, we examined how verification accuracy varied as the transformed templates were used for matching. When evaluating the changeability, we measured the dissimilarities between the original and transformed templates, and between two differently transformed templates, which were obtained from the same original fingerprint. The experimental results show that the two criteria mutually affect each other and can be controlled by varying the control parameters of the changing functions.

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.

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.]

In recent years, biometric, or "who you are," authentication has grown rapidly in acceptance and use. Biometric authentication offers users the convenience of not having to carry a password, PIN, smartcard, etc. Instead, users will use their inherent biometric traits for authentication and, as a result, risk their biometric information being stolen. The security of users' biometric information is of critical importance within a biometric authentication scheme as compromised data can reveal sensitive information: race, gender, illness, etc. A cancellable biometric scheme, the "BioCapsule" scheme, proposed by researchers from Indiana University Purdue University Indianapolis, aims to mask users' biometric information and preserve users' privacy. The BioCapsule scheme can be easily embedded into existing biometric authentication systems, and it has been shown to preserve user-privacy, be resistant to several types of attacks, and have minimal effects on biometric authentication system accuracy. In this research we present a facial authentication system which employs several cutting-edge techniques. We tested our proposed system on several face databases, both with and without the BioCapsule scheme being embedded into our system. By comparing our results, we quantify the effects the BioCapsule scheme, and its security benefits, have on the accuracy of our facial authentication system.